{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as pl\n",
    "import scipy.signal as signal\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../ai-sdr\")\n",
    "\n",
    "from Data.opnr import find_data\n",
    "from Data.rawdata import ChannelizedData\n",
    "from LTESync.ltesync import LTESync\n",
    "from LTESync.afc import AFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = find_data(\"../data\",\"2140.0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fn5 = find_data(\"../data\",\"2150.0\")\n",
    "# aLTE = ChannelizedData(str(fn5))\n",
    "# aLTE.channeling(2140e6,4)\n",
    "# aLTE.toDisk()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "aLTE = LTESync(str(fn),centered=False)\n",
    "aLTE.read(end=100e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x129468390>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,fd = aLTE.spectrum()\n",
    "pl.plot(f,np.abs(fd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "aLTE.decimate(aLTE.f_center)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "270: fdd 23.05556297302246 @ freq=2140000000 off=5531 pid=0 0\n",
      "[(np.float64(2.8635175261882395), 0, np.int64(703)), (np.float64(2.020804696186617), 1, np.int64(4243))]\n",
      "327 24.95536893606809\n"
     ]
    }
   ],
   "source": [
    "ok = aLTE.sync()\n",
    "if ok:\n",
    "    print(aLTE.cell,aLTE.cell.pid)\n",
    "    df,amp = aLTE.afc()\n",
    "    print(df,np.abs(amp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from LTE.lte import SSInd,ExtractResources,ofdm_rx_at\n",
    "# from LTESync.sequence import PSS,SSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dt = -137\n",
    "# to = aLTE.cell.off - 9600 + 960 - 137\n",
    "# pss = PSS(aLTE.cell.cid%3)\n",
    "# pss_fd = ofdm_rx_at(aLTE.waveform,to,128,4)\n",
    "# pss_ex = ExtractResources(SSInd(),pss_fd)\n",
    "# sss_fd = ofdm_rx_at(aLTE.waveform,to+dt,128,4)\n",
    "# sss_ex = ExtractResources(SSInd(),sss_fd)\n",
    "# dm_pss = pss_ex*pss.ofd.conj()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for nid2 in [0,1,2]:\n",
    "#     pss = PSS(nid2)\n",
    "#     dm_pss = pss_ex*pss.ofd.conj()\n",
    "#     pl.subplot(1,2,1)\n",
    "#     pl.plot(dm_pss.real,dm_pss.imag,'.')\n",
    "    \n",
    "#     dm_fd = np.zeros((2048,),dtype=np.complex64)\n",
    "#     dm_fd[:128] = np.fft.fftshift(pss_fd) * pss.fd.conj()\n",
    "#     cor = np.abs(np.fft.ifft(dm_fd))\n",
    "#     pl.subplot(1,2,2)\n",
    "#     pl.plot(cor)\n",
    "#     inx = np.argmax(cor)\n",
    "#     print(nid2,inx,np.abs(np.mean(dm_pss)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# slot0 = AFC.refs[384].ref[0][5,:]\n",
    "# xc = np.abs(signal.correlate(aLTE.waveform,slot0,'valid'))\n",
    "# pl.plot(xc)\n",
    "# pos = np.argmax(xc)%19200\n",
    "# print(pos,aLTE.cell.off-pos,aLTE.cell.dup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "aLTE.read(end=-1)\n",
    "aLTE.decimate(aLTE.f_center+df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "270: fdd 22.832687377929688 @ freq=2140000327 off=5531 pid=0 0\n",
      "[(np.float64(2.6269155173302314), 0, np.int64(5640)), (np.float64(1.9044191235025703), 1, np.int64(9460))]\n",
      "-6 109.1659647778106\n"
     ]
    }
   ],
   "source": [
    "ok = aLTE.sync()\n",
    "if ok:\n",
    "    print(aLTE.cell,aLTE.cell.pid)\n",
    "    ddf,amp = aLTE.afc()\n",
    "    print(ddf,np.abs(amp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref_frame = AFC.refs[aLTE.cell.cid].frame_refsignal(aLTE.cell.pid)\n",
    "# xc = signal.correlate(aLTE.waveform,ref_frame,'valid')\n",
    "# pl.plot(xc.real)\n",
    "# pl.plot(xc.imag)\n",
    "# pos = np.argmax(np.abs(xc))%19200\n",
    "# print(pos,aLTE.cell.off-pos,aLTE.cell.dup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbuf = np.zeros((19200,),dtype=np.complex64)\n",
    "for s in range(0,len(aLTE.waveform)-19200,19200):\n",
    "    rbuf += aLTE.waveform[s:s+19200]\n",
    "r = AFC.refs[0].all_peak(rbuf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(np.float64(56.83290736775999), 384, 0, np.int64(5531)),\n",
       " (np.float64(39.88400735120685), 270, 0, np.int64(5531)),\n",
       " (np.float64(30.283280265041416), 383, 0, np.int64(5531)),\n",
       " (np.float64(28.351100816520123), 270, 1, np.int64(5531)),\n",
       " (np.float64(19.49302064686606), 382, 0, np.int64(5531)),\n",
       " (np.float64(15.065031314252272), 262, 1, np.int64(5538)),\n",
       " (np.float64(13.590251036086785), 384, 1, np.int64(5531)),\n",
       " (np.float64(10.927477977333226), 33, 1, np.int64(4572)),\n",
       " (np.float64(10.251906453955383), 132, 0, np.int64(7456)),\n",
       " (np.float64(10.042832790297041), 465, 1, np.int64(16089))]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frame = AFC.refs[262].frame_refsignal(1)\n",
    "# xc = np.abs(signal.correlate(aLTE.waveform,frame,'valid'))\n",
    "# pl.plot(xc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from LTESync.sequence import Cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "aLTE.dec_factor = 1\n",
    "aLTE.decimate(aLTE.f_center+df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# c3 = Cell(262,nof_prb=100).cs_tx(1)\n",
    "# syms = c3.cs_put(0)\n",
    "# tx = c3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# xc = np.abs(signal.correlate(aLTE.waveform,c3.reshape(307200,),'valid'))\n",
    "# pl.plot(xc)\n",
    "# print(xc.argmax()%307200,aLTE.cell.off*16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gqPDFvlreKirj4x2VeP1BIDTOzayhGVw7MY/zh2Wg16qxunz86dNiXl53iKAC8XoN984ewg+mDzjt9ix7q+zc98Zmth+xAfCt8bk8fOVILHGnvpzY4w/w5b5aPtxWySc7KrG5jwaR1Hg9c0ZmceGIDMxGHYoSqiECUIDmvYdC5EbzrchjOUlG6VflDCHhQ4huFgwqfLyjkj9/uoe91Q4A0hL03DlzEN+b2g+jrmXjxQ0l9Ty6dCfbjoTGOilIi+fXlw9n1tCMU9ZmNNcSpCUY+PKBWRi0faNBpLXJxyWL11BudXPZmGz2VTkorrJjNmr557zJMT+1tKPcym/f38XaA3VAqGbg5xcN4dqJ+ZHTE/trHPy3qIy3vzlCpe3oFTNDM81cNymPq8blkm4+eQ3X9iNWfv3edjaF25sMzkjg0atGMW1garvLGAwqPP9VCX9YthuvP0iSScf/fms0l47O7tBn9fqDfLW/lo+2VfLxzkoaXdHpnr4wPZ6LRmYxZ0QmY/tQQ1e3L8DBWicHapz4AkHG5FkoSIvv9TWSp0vChxBhwaBCky+AyxvAHZ43+QI0eQOkJujpn2rq1E5cURRWFVfzp0/2sKM8dLRpidNx+3mFzJs2oNXLYINBhbe+KeOxZcXUOkLtAM4bks6vLx9+0nFavvXXL9l0uJF7Zw/m3tlDTni8N1u7v44b/7EucrScYTbw4q1TGJbVM34DFEXh051V/O+HuyI1WiOyE7l6fA7LtldGGqoCJJl0XDU2h+sm5TMyJ7FdO6JgUOGtojJ+v2w39eFTMVePy+GXlw4nI7H1UzFHGpv4+Ztb+Gp/KBzNHJrOY9eMafN1bfEFgqw7UMeH2yrZUFJPsLlzFNXRWfNna/6EqshjoRsKCgdrnfgCR3czGWYDF47IZM7ILKYVpnZpg9toCAYVKmxuDtQ4OFDj5GCtk/3h2+XWJo7fgyabdIzvl8yEfklM6JfMmPykU9aU9TUSPkS3UxSFQFDpkvPDHn+AGrsnMlU333Z4aHB6WwQKl9eP2xfE5fXj8gbwhKu9T0WtCo28WpgWz8D0BArTExiYHk9hegJpCfpWdxxf7qvlj58UR45YEwxabj2ngFvPLSCxA71m2t0+nlq1j399cRBfQEGjVjF3Wn/uvWAIFlNoPZsON/Ctv36FXqPmywfOP+VRdG+26KNd/G31AQrS4nnxlinkp/S8S4i9/iAvri3h/1bsxX7M6QmNWsV5Q9K5bmIe5w/POO1A2+jy8sdPinll/WEUJfQ/dd+FQ5g3rf8J3y1FUXh38xEefG8HdrefOJ2G/7lsON+b2q9HHXnb3D4+K67hkx2VfFZcg+OY9iVmg5ZZwzKYMzKT84akx6S3WUVRcHoDNDi91Dm9lNQ6OVDjYH+4RqOk1kmT79SDISYatRSmJ6BRq9h2xBo51dZMrYIhmWYm9E9mQjiU9NXakR4RPp5++mkef/xxKisrGTt2LE8++SRTpkxp83U9NXz4A0HUKlWPqi489kvT4PLS4PJFbus0aoZkmhmSmUCSqX0dMXVEvdPLlrJGtpSGpzIr9U4veq2aBIOWBIOWeIOWBIMmPD922dHb8QYN8Xotdo+PatvRUFFtC81r7J6ojUwap9MQp9cQp9Ng1KmptnlaNLQ7XqJRy8CMBArTEhiYEU9hWgKDMuKpd/p44tM9kWp4o07ND84u4PYZhe3u9OpkDtY6+d0Hu1i+qwoIHUH9dM5QvjM5nwVvbGHJlnKumZDHn64fe9rv0ZMpisK6A/WMyk3s8V2e1zu9PLlyL7sr7Mwals7V43I7XdNwrK1ljfz6vR2RhqrDssw8etWoyCmoBqeX/3l3Gx9uqwRgfL8k/nz9uDb7S4k1jz/A2v11fLKzik93VkWuAAPQa9ScPSiVOSOymD0igwxz+7dnIKjg9QfxBoJ4/UGcHj/1Li+NLi8NTl/499FLvdMXWnbM8kaXD2+g9YMUrVpFv1RT6LcgPZ7C8AFKYVo8KfFHD1K8/iA7K2x8c6iBbw43sOlwY+TKqWMlmXSMzw/VjIzvl4wlToc/GCSoKPgDoQO5gKLgDyoEg6F54CSTgoJOo45Meq0KvUaDTqNCp1WjjzymQn/sfW1oWbRP3cY8fLz++uvMnTuXZ599lqlTp7J48WLefPNNiouLychofTCprgwfHn8Aa5MPW5MPa5OPRldofvxkO8kyt+/oP6dGrUKjUoXmahVqFZHbzY+pj7mt06gx6tQYtBoMx8yNkftqjDoNBm3osebnajUqbE2+SLBodHmpd4a+LM3ztr40AJmJBoZkmhmaaWZIVmg+ODMBk759VYFN3gA7yq1sDoeMLaWNHK53nfbf4XToNWrSzQbSzAYyzAbSzQbSEwykJugx6bXE6TSY9BqM4XnL21oMWvUJwVFRFGocHvZXOzlQ6zg6r3FQ1nBiderJynTj1H7cNWtgh34o2/L53hoeXboz0nZkaKaZ/TUO/EGF9+8557Qu0RW9TzCo8MbGUv6wbDcN4bYX3x6fy6xhGfzm/Z1U2z1o1Sp+csFg7pw5sNddlRIMhjp5+2RnJZ/sqOJgrTPymEoFY3ItmPRavIEgvnCo8PqDeI4JGc2BIxojFuu1alJMevqlmMLhInTAUZgeT36K6bQbAVfZ3C3CyNaT1I7EQkFaPKt+NjOq64x5+Jg6dSqTJ0/mqaeeAiAYDJKfn88999zDAw880Opruyp87Cy3celfPo/a+nqS5i9NkklHSryeZJMel9fPnirHSVN3s34pplAoyUoIz80MSI2npM7JltJGNpeGgkZxlf2kX+7C9HjG5SUxNj809Usx4fL6cXoCODx+HB4/zmPnbj8O7zG3PQGcHj9Orx+zUUt6goGMRCPpCaFwEQkZZgOWOF23VlO6fQFK6pyhQFITCiQHap3sr3bgDQS5dmI+95w/6KQDmkWDPxDklfWH+fOneyI1P1MGpPDGHdO65P1Ez9Xg9PL4J8W89vXhFoF4UEYCT1w/rk+MSaMoCvtrHHy8o4pPdla1uDT5dJj0GpJNepLjdaG5SU+ySUdy+Pfx2N/K5ttxOk23/MYcXzvSfKrm+ANYjVqFVtPyQPfopEYbPpjyHRPOfAEldDu8zOdXQrebA1sgGPkfGpKZwCf3nRfVzxbT8OH1ejGZTLz11ltcffXVkeXz5s2jsbGR9957r8XzPR4PHs/RqjebzUZ+fn7Uw0dZg4tz/rAKlQoSjToscS2nxLgTl1nidCSZQvMEgxYFjlZ5KaHqsGNvN1eNBZWjc38gtNzjD+D2BfH4A3h8Qdy+UHsEj//Y283PCeLxBfAFgpiNoS9Gkin8JYoPf4mOud3al8bu9rGnysGeKjvFlXb2VIWmWoe3Q9sv3WxgXH4S4/KTGJuXxOg8S6uX7/VVSrgqtLNde7dXg9PLE8v38PneWv543ZhOd3Yleq8tpY38+r3tbDti5eazC7j/4qEnXEXVV1Ra3aw/GDqtqdeoQ6cMwqcNmm8btGr0Gs3RxyKnFVR9sj1FtASCoYDiDypRbwgb0/BRXl5Obm4uX331FdOmHT1Ku//++1m9ejXr169v8fyHH36YRx555IT1RDt8BIIKDo8fs0Hbo9ptxEqtwxMKIpV2isPhZE+lHbvHT7xew5hwjca4fAtj85PISjTKF1qIGFMUBbvH36EGzUJ0l46Ej5hf/7Nw4UIWLFgQuW+1WunXrx82my3q76UCHNFpu9jr6YFR6XpGpafC6FB/AoqiUO/0kmTSH9e9sg+7XTacED2FzXvq06lCxErzfrs9dRpRDx9paWloNBqqqqpaLK+qqiIrK+uE5xsMBgyGo5cNNhc+P797ulIWQgghRPTY7XYsltbbIkU9fOj1eiZOnMiKFSsibT6CwSArVqxg/vz5bb4+JyeH0tJSzGZz1Kv5m9uTlJaW9qjLeHsS2Uatk+3TNtlGbZNt1DbZRq3ridtHURTsdjs5OTltPrdLTrssWLCAefPmMWnSJKZMmcLixYtxOp3cfPPNbb5WrVaTl9e1Y1YkJib2mD9WTyXbqHWyfdom26htso3aJtuodT1t+7RV49GsS8LHDTfcQE1NDQ8++CCVlZWMGzeOZcuWkZnZ/YNDCSGEEKJn6bIGp/Pnz2/XaRYhhBBCnFl6V5d4nWQwGHjooYdaNHAVLck2ap1sn7bJNmqbbKO2yTZqXW/fPj1uYDkhhBBC9G1nVM2HEEIIIWJPwocQQgghupWEDyGEEEJ0q5h3r368YDBIeXl5l3QyJoQQQoiucWwnY2p163UbPS58lJeXS9fqQgghRC9VWlraZmehPS58mM1mgB7VZawQouvZ3D7+9flBLh6VxbBs+e4L0ds0d/nevB9vTY8LH82nWnpal7FCiK7158928MKGKt7cWsdLP5zKuPykWBdJCHEa2tNkQhqcCiFirtbh4T8bDgNg9/j5/j/Xs7WsMbaFEkJ0GQkfQoiYe/7Lg7h9QUblJjJ5QDJ2t5+b/rGebWXWWBdNCNEFJHwIIWLK5vbx4leHALjn/ME8f/MUJvVPxub2c9M/17P9iAQQIfoaCR9CiJh6ae0h7B4/QzITuHB4JgkGLS/cMoWJ/ZOxNvn43j8kgAjR10j4EELETJM3wL++OAjAXTMHoVaHGqolGLS8cPNkxvdLwtrk46Z/rmdnuS2WRRVCRJGEDyFEzPxnw2HqnF7yU+K4fEx2i8fMRh3/vmUKY/OTaHT5+N4/1rGrQgKIEH2BhA8hREx4/UH+vuYAAHecNxCt5sSfo0SjjhdvmcLYPAsNrtApmOJKe3cXVQgRZRI+hBAx8e6mI1RY3WSYDVwz4dS9IVridLx461TG5Fmod3q58bl17KmSACJEbybhQwjR7QJBhWdW7wfgtnMLMeo0rT7fEqfjpVumMio3kbpwANkrAUSIXkvChxCi2320vYKDtU4scTpunNqvXa+xmHS8fOtURuYkUuvw8t3n1rOvWgKIEL2RhA8hRLdSFIWnV4VqPW6ePoB4Q/tHeUgy6Xnlh1MZkZ1IrcPDd/6+nn3Vjq4qqhCii0j4EEJ0q8+Ka9hVYcOk1/CDswd0+PXNAWRYlplah4cbn1vHgRoJIEL0JhI+hBDdRlEUnlq1D4CbzupPkkl/WutJjtfz6m1nMSzLTLXdw3efW8fBWmc0iyqE6EISPoQQ3ebrg/UUHWpAr1Hzw3MKOrWulPhQDcjQTDNVNg/f/fs6GpzeKJVUCNGVJHwIIbpNc63HdZPyyEg0dnp9qQkGXrltKoVp8VTa3Dy7Zn+n1ymE6HodCh+LFi1i8uTJmM1mMjIyuPrqqykuLm7xnJkzZ6JSqVpMd9xxR1QLLYTofbaWNfL53lo0ahV3nDcwautNSzDwq8uHA/Dvr0qotrujtm4hRNfoUPhYvXo1d999N+vWrePTTz/F5/MxZ84cnM6W51pvu+02KioqItNjjz0W1UILIXqfv4avcLlqbA75KaaornvW0AzG90vC7QtG3kcI0XO1/xo3YNmyZS3uv/DCC2RkZFBUVMSMGTMiy00mE1lZWdEpoRCi19tbZWfZjkoA7pwZvVqPZiqVip/NGcr3/rGeV9cf5kczCslJiov6+wghoqNTbT6s1tAw1ykpKS2Wv/LKK6SlpTFq1CgWLlyIy+U65To8Hg82m63FJIToW5p7M71oZCaDM81d8h5nD0zlrMIUvIEgT67c1yXvIYSIjtMOH8FgkHvvvZfp06czatSoyPIbb7yRl19+mVWrVrFw4UJeeuklbrrpplOuZ9GiRVgslsiUn59/ukUSQvRApfUu3ttcDsBdMwd12fuoVCp+OmcoAG9uLOVw3akPeoQQsaVSFEU5nRfeeeedfPTRR3zxxRfk5Z16UKiVK1dywQUXsG/fPgYOPLG61ePx4PF4IvdtNhv5+flYrVYSExNPp2hCiB7k1+9u56V1hzh3cBov3Tq1y99v7r++Zs2eGq6ZkMefrh/b5e8nhAix2WxYLJZ27b9Pq+Zj/vz5vP/++6xatarV4AEwdWrox2bfvpNXgxoMBhITE1tMQoi+odru5vWNpUDX1noc66cXDgHgnU1l0vW6ED1Uh8KHoijMnz+fd955h5UrV1JQ0HYnQZs3bwYgOzv7tAoohOi9/vnFQbz+IBP6JXFWYUrbL4iCsflJXDgik6ACi5fv6Zb3FEJ0TIfCx913383LL7/Mq6++itlsprKyksrKSpqamgDYv38/v/nNbygqKqKkpIQlS5Ywd+5cZsyYwZgxY7rkAwgheiary8fLaw8BcPesQahUqm577wXh2o/3t1awq0IasQvR03QofDzzzDNYrVZmzpxJdnZ2ZHr99dcB0Ov1LF++nDlz5jBs2DB++tOfcs0117B06dIuKbwQoud64asSnN4Aw7LMnD8so1vfe3h2IpePCdW2/vlTqf0QoqfpUD8fbbVNzc/PZ/Xq1Z0qkBCi93N6/Dz/1UEA7urmWo9m984ewofbKvh0ZxVbShsZm5/U7WUQQpycjO0ihIi6174+TKPLx4BUE5eNjk17r0EZCVw9PheQ2g8hehoJH0KIqPL4A/x9zQEg1JupRt39tR7NfnLBYLRqFav31LChpD5m5RBCtCThQwgRVf8tOkK13UO2xci3xrd+KX5X658az3WTQh0X/vHj4jZPHQshuoeEDyFEVNjcPp5etY/ff7QLgNvOLUSvjf1PzD3nD0KvUbP+YD1f7a+LdXGEEHSwwakQQhyvzuHh+S9L+PfaEuxuPwAjshP5zpSeMVRCTlIcN07txwtflfDHT4o5e2BqTBrACiGOkvAhhDgtFdYmnltzkNe+PkyTLwDA4IwE7po1kCvG5KDVxL7Wo9ldswbynw2H2XS4kVXF1Zw/LDPWRRLijCbhQwjRISW1Tp5dvZ//flOGLxBqQzE618LdswYxZ0Qm6hg2MD2VDLORedMG8Lc1B/jTJ3uYNTRDaj+EiCEJH0KIdtldaeOvq/bz/tZyguF2m1MLUrh71iDOHZzW43fmt583kJfXHWJHuY2Pd1Ry8SgZ8kGIWJHwIYRo1abDDTy9aj/Ld1VFls0ams7dswYxaUD3jNcSDSnxem49p4C/rNzHnz/dw4UjsmJ6GbAQZzIJH0KIEyiKwtr9dTz92T6+3Be6QkSlgktHZXPXrIGMzLHEuISn59ZzC3nhqxL2VDl4f2s5V43LjXWRhDgjSfgQQkS4fQGWbCnnhS9L2BkekE2rVnH1+FzunDmQgekJMS5h51jidNx+3kAe/7iYJz7dw2Wjs3tUw1ghzhQSPoQQVFrdvLzuEK9+fZh6pxcAg1bNDZPz+dGMQvKSTTEuYfT84OwB/POLg5TUuXj7myNcP7lnXBLclRRF4ct9dSTH63ptrZXoWyR8CHGGUhSFbw438PyXJSzbXok/3Io0x2Jk7tkDuGFSPsnx+hiXMvriDVruPG8gv/twF/+3Yi9Xj8/tEZ2hdZXdlTYeXrKDdQdC3ctfMTaH+y8aSn5K3wmUoveR8CHEGcbjD/DB1gqe/7KEbUeskeVTClK4+ewBXDgis8+firjprP489/kBjjQ28frGUr5/Vv9YFynqrC4fTyzfw0vrDhEIKug1anzBIEu3lPPxjkpunj6Au2cNItGoi3VRxRlIpfSwwQ5sNhsWiwWr1UpiYmKsiyNETAWDCptKGyg61ECSSU+2xUi2xUiWJY4EQ8eOHaptbl5ef5hX1x+i1hE6taLXqrl6XA7zzh5wxlXHv7i2hAff20FmooHVP5+FUacBQtvc5vZR7/RGpgaXl3qnjwaXlzpH830vLq8fvVaNQavBoFWHbx+9b9CFbp9s+di8JEbmJEb9EuVAUOGNjaU8/nFx5BTaxSOz+J/LhmNt8vG7D3ax9kCoEXFKvJ57Zw/mu1P6oeuCwBkI16bJVUVnho7svyV8CNHDKIrCjnIbS7eU8/7WCo40Np30eWaDliyLkaxjAkn2MfezE+NIjNOypczKC18e5INtFZFOwbISjXx/Wn++Mzmf1ARDd368HsPjD3D+H1dzpLGJoZlmgooSCRrBbvpVHJZl5vpJ+Vw9PpeUKJziKjrUwMNLdkRqtAZlJPDwFSM5Z3Ba5DmKorBydzX/++Eu9tc4AShMj2fhJcOZPbzzna81eQN8VlzNh9srWbmrCo1axZXjcrhuYj5j8iw9vj8YcfokfAjRC+2tsrN0SzlLt1ZwsNYZWZ5g0DJ9UCpNviCV1iYqrO7IGCptMWjVePzByP1J/ZP5wfQBXDQyq0uOdHubNzaUcv9/t570MbNBS3K8npTwlGzSk5oQmqfE60iJNxCv1+ANBPH4w5MvcPS2P4A3sjx0v/kxW5OPtQfq8Ib/NjqNitnDM7luUh4zBqd3+LRXtc3N7z/azdubjkTKfu+FQ5g7rf8p/86+QJD/bChl8ad7qAvXkJxVmML/XDqC0XkdqwVzef2s2l3Dh9sqWLm7OtLd/vEGZyRw7cQ8vjUhlwyzsUPv0R3cvgAGrVoC0mmS8HESiqKwr9pB/9T4Pt24rCMCQYWDtU52VtjYUW5lZ7mNAzVOUuL1FKbHU5iWEJqHb8fpNbEu8mkJBBVsTT4am3w0urw0NvmwunyoVGDSa4nTaYjTazDpNcTpwvPw7a5u+3Cozsn7WytYuqWc3ZX2yHKDVs3s4ZlcMTabmUMzIqcEmjk8fiqtbiqtbiqsTaG5rfm+m0prEw0uHwB6jZorxubwg7MHdHin0tcpisKnO6vwBoKkmPQkx+tJjdeTZNJ3+e+E1eVjyZYjvLGxrEXbmwyzgWsm5nHdxDwK27i02esP8vyXB/nLir04vaEd/vWT8vj5RcNIN7evRsvm9vHMZ/v55xcHI2Ho2+Nz+dlFQ8lJijvl65wePyt2V/PRtgpWFVfj9h0NuXnJcVw6OptLRmXh9AR4q6iUj7ZXRoKwRq3ivCHpXDsxjwuGZ2DQdu9vi8cfYH+1k+IqG7sr7RSHpwqrG6NOTV6yifzkOPJTTOQlx5GfbCI/xUR+sonEOO1phxNFUbA2+agLn86rc3hpdHlJMulC608x9eo2OD0ifDz99NM8/vjjVFZWMnbsWJ588kmmTJnS5uu6KnzU2D1M/t1ytGoVBWnxDM5MYHCGmSGZZoZkJjAgLT6mR4KKouDxB3F5AzT5AjR5Q0dNZqMWi0mH2XD6//AQqgotrrJHQsbOChu7K+ynPEI5mRyLkcL0cCBJi4/czrHEnXQ8j2BQwe72Y23y0djkxdrki0yNLh+28G2b24cKFTqNCp1GjU6rRq9RH72vCZ1Lb3Ffo0anVREMEl6/D2s4WDS4jt5udIXWf7r/5XqNOhJETHoNRp2GBIOW1ITQUXBagoHUBAPpCXpSEwzh+/pW/17ljU18sLWCpVvL2Vp2dKej04R+kK8Ym8MFwzM73KbjeG5fgEqrm2STHoup9/6gnQl2Vdh4c2MZ724+EmmnAaGaqusm5XHZmJwT/h8+K67m0aU7ORCuJRubn8QjV45kXH7SaZWhrMHFHz8u5t3N5UAoAP/w3ALunDko8t52t4+Vu6v5YGsFq/fUtKhV65di4tLR2Vw6OovRuSeeXrG5fXywtYI3N5byzeHGyPIkk46rxuZw3aT8qLeBCQYVyhqa2F1po7jSzu6qUMg4WOuMtEfpKLNBS15KKJzkJZvITwmFE7NRGwoUx7QVqnV4IrfrnF4anN7IVWWnkmTShcNOXCTw9AsHk9ykuB598Bzz8PH6668zd+5cnn32WaZOncrixYt58803KS4uJiMjo9XXdlX42FrWyI3PrcfhOXl1tU4TDiUZZgZnJkRCSf/UU4cSRVFwePzY3H6srqM7Vpv76I7V2uTD4fHj9gVCwcIbOHo7HDKafKGptb+ERq3CEqcjKU6HxRSaN+9UkuL0JJl0JJl0oeeY9Djc/lDQqLCxs9zG/hrHSc9jG3VqhmcnMiI7kRE5iQzJNFPn8HKg1sGBGicHahwcqHXSGD6KPhmDVk1BWjypCXpsTeGw4fJi9/hPe6ffFRIM2vD2CU2KAq5j/h5Hb/s7fc5fr1WTFq8nzWwgNT4UUpLj9Ww63MCGkobI89QqmD4ojSvG5HDRyCwJCWc4rz/Iyt1VvLGxjM+KqyP/h3E6DZeNyea6iXlkJhr57Qe7It3dpyXouf/iYVw7IS8qg/ptKW3kdx/s4uuS+sj6bzqrP9uP2FiztyZSOwIwILU5cGR3KDjsr3HwVlEZb39TRpXNE1k+LMvMtRPzuHp8LmmnaIukKApNvgC2Jn/ktzY0P3q/tL6J4io7e6rsuLwnP8BKNGoZlpXI0CwzQ7PMDMsyMzA9AWuTj9IGF2UNTZTWuygNz8samqh1eE66ro4yG7SkJIRO5yXF6ah3+Sird0VOf52KSgXZiUbyUkKBJMNsQKUCRQEFCIZvKOHtpERug4IS+T1WFIXUBAM/vmBwVD5Ps5iHj6lTpzJ58mSeeuopAILBIPn5+dxzzz088MADrb62K9t8KIpChdXNnio7e6sc7Kmys6fawb4qe6TK8ng6jYrCtAT6pZpw+wKhcBEJGf7TTs+taT7a1mnUODy+FtWZnZEar2dETihkjMyxMCI7kYK0+Ha1RK93ekNBpMbJ/mOCyeF6V6QR46nE6TSRnX5iXCggRYJUXGiZShX64fUFFHyBIL5AEK8/iDd82+cPLY/cDz8POBooIiEs9IVuDhlJJj2WOF27a7aaa6FODCWhYOLw+ENHNXYPteF5Xfgop87hPWXAPdaUASlcMTabS0Znn/JHVpzZqmxu3v7mCG9uLI3UbhxLq1Yx7+wB/GT24KhX1SuKwic7q/j9R7tbtD+CUOPUy0Znc8mobIZnmztVUxEIKny+t4a3isr4ZGdVJNho1SrOGZyGSa85LmT4sTX52qw9OJZeq2ZQegLDwiEjFDQSyUw0dLjsTd4AZc3BpMEVCif1TZQ1unB6ApH2QanHtBVKSzAcXR4OHKc6zeTw+ClrcHG47mjoCQUgF4frXVHbF0Do77jypzOjtj6Icfjwer2YTCbeeustrr766sjyefPm0djYyHvvvdfi+R6PB4/naJq02Wzk5+d3a4NTRVEoj4QSO3uqHOytsrO32nHK1HwsvUYd3qlqIzvWY3ey8QZtpD1Bc9sCo665Kj/U5sCoV2PSazFq1Se0M2gOPY2ulm0WGo45tWBt8oYfDz1Hr1WHgkZ2OGjkJIZTcnQbUvkDQcoamjhQ68Da5It85tCkJzFO2+3nc2OtyRugzumh1uGlzuGh1tF820tOkpHLxmSTbTn1uXQhjtXcGdwbG8p4f2s5Tm+Acwal8dAVIxicae7S9/b6g7yy/hCr99QwJi+Jy0ZnMyQzoUsaZFpdPpZsLeetjaVsOeZ05Klo1SoS43QkGrXhuY7EOC2JRh0ZZgNDw7UaA1JNfaLfGkVRqHV4jwk9rsgl8yoVqFUqVOHbqvBtVKBCFVrW/Bgq1CpIjtdz8/SCqJYxpuGjvLyc3NxcvvrqK6ZNmxZZfv/997N69WrWr1/f4vkPP/wwjzzyyAnr6QlXuwSDCuXWJvZWOShrcBEfrrZPbLGD1Z3QGFAIIbqCy+unzuElLzmuT1+RsafKzpo9Nei16hah4tiQEafT9Olt0Bt1JHzEvIfThQsXsmDBgsh9q9VKv379sNlsMSzVUYkamJhjZGLO8ZeFKYAXb5MX78m7YRBCiKizaMFut7f9xF4sKw6uH5t2kkdCv7t+txe7u7tLJdrSvN9uT51G1MNHWloaGo2GqqqqFsurqqrIyso64fkGgwGD4eg57+bC5+f3/cGehBBCiL7GbrdjsbR+WX/Uw4der2fixImsWLEi0uYjGAyyYsUK5s+f3+brc3JyKC0txWzuXEOmk2luT1JaWhrzUzo9lWyj1sn2aZtso7bJNmqbbKPW9cTtoygKdrudnJycNp/bJaddFixYwLx585g0aRJTpkxh8eLFOJ1Obr755jZfq1arycvL64piRSQmJvaYP1ZPJduodbJ92ibbqG2yjdom26h1PW37tFXj0axLwscNN9xATU0NDz74IJWVlYwbN45ly5aRmZnZFW8nhBBCiF6kyxqczp8/v12nWYQQQghxZun9Fz93gMFg4KGHHmrRwFW0JNuodbJ92ibbqG2yjdom26h1vX379LiB5YQQQgjRt51RNR9CCCGEiD0JH0IIIYToVjHv4fR4wWCQ8vLyLunnQwghhBBd49h+PtTq1us2elz4KC8vl95NhRBCiF6qtLS0zf66elz4MJtDozT2pF7bhDjTKIrC/hoHhWkJqNVSAymEaFtzr6vN+/HW9Ljw0Xyqpaf12ibEmeThJTt44asSbp9RyMJLh8e6OEKIXqQ9TSY61OB00aJFTJ48GbPZTEZGBldffTXFxcUtnjNz5kxUKlWL6Y477uhYyYUQMfOfrw/zwlclAPzzi4Psq+7bI6gKIbpfh8LH6tWrufvuu1m3bh2ffvopPp+POXPm4HQ6Wzzvtttuo6KiIjI99thjUS20EKJrbCip59fvbQcg3WzAH1R49P1d7RoiWwgh2qtDp12WLVvW4v4LL7xARkYGRUVFzJgxI7LcZDKRlZUVnRIKIbrFkcYm7ny5CF9A4bLR2fzsoqHMeWI1a/bUsHJ3NRcMl7GZhBDR0al+PqxWKwApKSktlr/yyiukpaUxatQoFi5ciMvlOuU6PB4PNputxSSE6F5N3gA/enEjtQ4vI7ITefy6MRSkxXPLOQUA/Ob9nXj9wRiXUgjRV5x2+AgGg9x7771Mnz6dUaNGRZbfeOONvPzyy6xatYqFCxfy0ksvcdNNN51yPYsWLcJisUQmucxWiO6lKAo/f2sLO8ptpMbr+fvciZj0oUrRe84fTLrZQEmdi+e/PBjjkgoh+orTHtvlzjvv5KOPPuKLL75o9XrelStXcsEFF7Bv3z4GDhx4wuMejwePxxO533ypjtVqlatdhOgGT6/ax+MfF6NVq3j1trOYUtCyJvPNjaX8/K2tJBi0rPzZeWSYjTEqqRCiJ7PZbFgslnbtv0+r5mP+/Pm8//77rFq1qs2ORKZOnQrAvn37Tvq4wWCIXFYrl9cK0b2W76zij5+Erlh75KqRJwQPgGsm5DE2z4LD4+ePHxef8LgQQnRUh8KHoijMnz+fd955h5UrV1JQUNDmazZv3gxAdnb2aRVQCNE19lbZuff1zSgK3HRWP743tf9Jn6dWq3jwipEAvFlUxtayxm4spRCiL+pQ+Lj77rt5+eWXefXVVzGbzVRWVlJZWUlTUxMA+/fv5ze/+Q1FRUWUlJSwZMkS5s6dy4wZMxgzZkyXfAAhRMdZXT5ue3EjDo+fqQUpPBQOF6cysX8y3xqfi6KEOiCTS2+FEJ3RofDxzDPPYLVamTlzJtnZ2ZHp9ddfB0Cv17N8+XLmzJnDsGHD+OlPf8o111zD0qVLu6TwQoiO8weCzH/tG0rqXOQmxfHX701Ap2n7p+AXFw/DpNfwzeFGlmwp74aSCiH6qg7189HW0U5+fj6rV6/uVIGEEF1r0Ue7+XxvLXE6Dc/NnURqgqFdr8uyGLl71iAe/7iYRR/u5sIRmZGrYoQQoiM61c+HEKJ3eauojH9+Ebpk9s/Xj2VETscaeN96TgH5KXFU2tw889n+riiiEOIMIOFDiDPEN4cb+OXb2wD48QWDuWR0xxuBG3Ua/ufSEQD8bc0BSutP3YGgEEKcioQPIc4AlVY3t79UhDcQ5KKRmdx7weDTXtdFIzM5e2AqXn+Q//1wVxRLKYQ4U0j4EKKPc/sC3P7SRmrsHoZmmvnz9eNQq9se8vpUVCoVD14xArUKPtpeyVf7a6NYWiHEmUDChxB9mKIoLHx7G1vKrCSZdDw3dxLxhs43Eh2WlchNZ4X6BXl06U78ARn3RQjRfhI+hOjDnvv8AO9sOoJGreKvN06gX6opauu+b/YQLHE6dlfaeW1DadTWK4To+yR8CNFH7au284dloe7QH7x8BGcPSovq+pPj9fx0zhAA/vxJMY0ub1TXL4TouyR8CNFH/f6j3QSCCrOHZzJ32sm7Tu+sG6f0Y0hmAg0uH4uX7+2S9xBC9D0SPoTog9YdqGP5rmo0ahULLx2GSnX6DUxbo9WoI12zv7TuEHuq7F3yPkKIvkXChxB9TDCoRC6B/e6UfAamJ3Tp+00flMZFIzMJBBUeXbpTxn0RQrRJwocQfcz72yrYWmYlXq/hJxcM6Zb3/J9LR6DXqPliXy2f7qzqlvcUQvReEj6E6EM8/gCPLdsNwO3nDSTd3L5xWzqrX6qJH55bAMBvP9iFxx/olvcVQvROEj6E6ENeWnuIsoYmMsyGSBjoLnfPGkSG2cDheldk/BghhDgZCR9C9BFWl48nV+4D4KdzhnT7iLPxBi0PXDIMgCdX7OOz4upufX8hRO8h4UOIPuLpz/ZhbfIxJDOBayfmx6QMV4/LZcaQdJp8AW55YQMvfHlQGqAKIU4g4UOIPqC03sULX5YAsPCS4Wg6MXZLZ6jVKv4xdxLXTcwjqMDDS3fy6/e245Pu14UQx5DwIUQf8MdPivEGgpw9MJWZQ9NjWha9Vs1j145h4SXDUKng5XWHufn5DVibfDEtlxCi55DwIUQvt7Wskfc2lwPwy0uHd1mHYh2hUqm4/byB/O2miZj0Gr7YV8u3/volJbXOWBdNCNEDSPgQohdTlKMdin1rfC6jci0xLlFLc0Zm8eYd08i2GDlQ4+Tqv37JugN1sS6WECLGJHwI0YutKq5m3YF69Fp1ZJC3nmZkjoX37p7O2PwkGl0+vv/P9bwho+AKcUaT8CFEL+UPBFn0YahDsZvPHkBesinGJTq1jEQjr//oLC4bk40voHD/f7fyvx/uIhCUK2GEOBNJ+BCil3qzqIy91Q6STDrumjUo1sVpk1Gn4anvjucnFwwG4O9rDnD7SxtxevwxLpkQortJ+BCiF3J5/fz50z0A3HP+YCxxuhiXqH1UKhX3XTiE//vOOPRaNct3VXPNM19xpLEp1kUTQnQjCR9C9ELPrTlIjd1DfkocN53VL9bF6bCrxuXynx+dRVqCgd2Vdq566ks2HW6IdbGEEN1EwocQvUy13c3f1uwH4P6LhmHQamJcotMzoV8y782fzrAsM7UODzf8fR1LtpTHulhCiG6gUnpY38c2mw2LxYLVaiUxMTHWxRGix/nlO9t4df1hxuYn8e5dZ/eIfj06w+Hx85PXNrFid2gsmGyLEZNeg0mvJU6vCd/WEKfTHr2t1xB/3ONmo460BAPpZgNJcTrUMerlVYgzVUf239078pQQolP2Vdt5PXyZ6i8vGdbrgwdAgkHL3+dO4g/LdvP3NQeosLo7vU6tWkVagoE0s570cCBJNxsi4aR5WZrZgNmg7RPbUYjeRMKHEL3I7z8qJhBUmD08k6mFqbEuTtRo1Cp+eelwbp4+gBq7B5c3QJM3gMsbwOn1R243ef24vAFcvubH/ZHnWpt81Do8NLh8+IMKlTY3lba2g4xRp2bygBSuGJPDRaOyek3jXSF6MzntIkQXCwQV9lU72FlhxaTXMql/MqkJhg6vZ/2BOm74+zo0ahUf3zuDQRkJXVDa3s/rD1Ln9FBr91LjcFNj91Bj91Dr8EZu1zg81No92I+7zFevUTNjSDpXjsth9vAMTHo5PhOiveS0ixAxEggqHKhxsLXMyrYjoWlnuY0mX6DF8wamxzOlIIXJA0JTXnJcq1X/weDRbtS/Mzlfgkcr9Fo12ZY4si1xQOvdzTd5A5Q1uPhkZxVLNpdTXGVn+a4qlu+qIk6nYfaITK4Yk815Q9N7bcPezvIHgjg9ASwmqRES0SM1H0KcpmBQ4UCtk21HGtlWZmPbkUZ2lNtweQMnPNek1zAiOxFrk4+91Y4THs+2GJk0IIUpA5KZXJDCkAxziwaTS7eUc89rmzDpNXz285lkmI1d+tnOVMWVdpZuKWfp1nIO1bkiy81GLRePzOKKsTmcPTAVraZ7LhR0evxU2dxU2z2huS1UW5OWoCfDbCDdbCTDbCAj0dCpcOT2BSitd3GozkVJnZPD9S5K6lwcrnNS1tCEP6gwNs/Ct8bncsXYnNOquRN9X0f23xI+hGhDIKhQ3tjE4XoXh+td7Kt2sO2IlR1HrDhPEjTidBpG5SYyKtfCmDwLo3MtFKQloAmHiXqnl40l9WwoqWdDSQPbj1jxH9fNuCVOx6T+oSAysX8yC97YTGl9E/fNHsJPZg/uls99JlMUha1lVpZuKef9rRUt2o6kxuu5dHQ2V47LYWK/5FavqlEUhUBQIahAMHI7NG9w+SLBotrmPiFkVNs9ODrQ+6slTkdmooGMcCBJP+Z2ZmJo7vD4OVTn4lC9k0O14Xmdi0qbm/buCbRqFTOHZnDNhFzOH55xxtYIiRNJ+BDdqnnn7PIG6J9qwqjruh+jGruHHeVWdpTb2FFupcbuIdmkJzXBQHpCaJ6WYCA1QU9agp60BAOWOF2bVzO4fQEOh4/8DoWP/A7VhcJGWYMLX+DkXxOjTs3InFDAGB0OG4XpR4NGe7i8fjYfbuTrcCD55lDjCadpADLMBj77+Uxph9DNgkGFDSX1LN1azofbKql3eiOPJRi0aNQqgs2hQlEIBoncjsava4JBG6ndyEw0kmDQUufwUm1vDi4evIFgVN6nf6qJAanx9Es1MSDVRP/UePqnmtCq1XywtZy3Nx1ha5k18hpLnI7LxmRzzYRcJvRLjupVQ06PH48/iD8QxB9U8AcU/MGWt32BUJCLPCcYxN+87Jig1xz6/EGFYPh+QIFAMEgg2DIYJhi0JJv0JJl0JIXnySY9ljhdh77XZ6IeET6efvppHn/8cSorKxk7dixPPvkkU6ZMafN1Ej56rganlwO1Tg7UODhQ6+RgjZMDtQ5K6lx4/aEfP5UKcixxFKbHMzA9gcL0eArTQvOsRGO7+15QFIXS+qYWQWNHuY1qu6fD5daqVaQm6EmND4WS9AQDKfF6Gpt8HA4fBVbZWl+vTqMiP9kU/lGOZ1Q4bAxMj496FbwvEGRnuY0NJfV8fbCejYcaqHd6+b/vjOOqcblRfS/RMb5AkK/217F0Szkfb688ocFqRxwbKjLMRjLD4SI9XFPRXFsRb2g9bCqKgrXJFwki1XY3VeF5td1DzTHL4vSaowEjxcSAtHDASDGREq9vV3jYW2Xn7U1HeHfTkRaXRfdPNfHt8Xl8a3wu/VLbN8hh84HLvhoH+6sd7K9xsL/ayf4aB3XHhLyeItGoJTleT1LcicEk3qDBF2gZjJpDkS8QCkW+YDAclsLLwiEpwaAhyaQnOby+ZJOe5PjQe6SE75uN2tPuuyYYVHD7Q1eFuf1BmrwBVCoYmB7dtmMxDx+vv/46c+fO5dlnn2Xq1KksXryYN998k+LiYjIyMlp9bU8IH4HwP0vkHyYQJKhAvCHUsVFXdF7kDwRpcPmod3ojU4PLS5M3QJMvgNt3zDyyLHiSZaHlKiAxTkdinA5LnBZLnI5Eow5LXGhKjMy1R5cZdZgMWsobmzgQDhYHapwcDAeOBpfvlOXXa9UYtGrs7lP/GMfpNBSkxYcCSXoCA8PBpF+qiQprEzuO2CJBY2eF7aTrUqmgIC2ekTkWRuYkkpccR6MrdIllncPbYl7r8GBrpTzHMxtDR379U0JHfv1STPRPCQWObEtczI56FEXB6Q2Q0MZOSHQvty/UWBVUaNQqNCoVajWoVaH7apUKtSp0GbE6fP/Y5+i6qd1IVwkEFdYfqOO/3xzho+0VLdo6TRmQwrcm5HLp6GwscTqavAEO1DrYV+1gf40zHDIcHKx14vG3XmujVoFWrUarCW1XnUYdmqtVaDQqdOrQfa1GjVatCj0v/DdontQqFdrw3+Fkj2nUoFGrUanA7vbT6PLS6PLR2OSl0enrVMiMFrWKFgElyaQn3qDBc8x+oPn3PxI0wvdPVjM2JDOBT+47L6pljHn4mDp1KpMnT+app54CIBgMkp+fzz333MMDDzzQ6mu7KnwcrnNx16tF+Pyh9Hk0WDSn0iC+cOhobYuoVGA2aDEbdZiN2tAO3njM/fDcbAzt2M1GHYFgkHqnj3qn54R5c+CwNp16x96T5FiMFBxTm1GQFqrhyEmKQ60KtWeI1I7UONkfDjGH61wntGtoi16jZkhWAiOzLYzMTWRkTiLDshLbPBI8lscfoN7ppc7hpaZFQPGQaNTRL/XokV+Sqe3TM0KIE7m8fj7eUcnb3xzhi321kd9QvVZNeoKh1YED9Rp16HckI/RbMigjgYHpCfRPNXXZwV5H+QJBrE2+SChpcPlaBJQGl48mbyAcftToNCq06vC8xe1wQDrmeRq1Gof76DobXD4aXKGDzwZnaNnJ2padLr1WjVGrpjA9gXfvnh619UKMw4fX68VkMvHWW29x9dVXR5bPmzePxsZG3nvvvRbP93g8eDxHq7xtNhv5+flRDx8Ha53M+uNnp/ValSp0pBLo4M7zdN4nKU5HSryelPhwsg13JW3UaYgLT0adBqP+6P04vRqjtuWygKJga/Jhc/uxNvmwNvlC95tvu5uXHfO424eihMJVc+1Ec01FQVpoOt32Br5AkNJ6V4salebbtQ4v8XoNI3ISIzUaI3MsDMpIQK/t3UeHQpxpKq1u3tt8hP9+U8aeqqNXdqXE6xkYPh07MD0hEjbykk3SlqINHn8gHHpCgaQ5nDR5Axgi+wX10f2DTh2eH913GHVqDFpNl27rmPbzUVtbSyAQIDMzs8XyzMxMdu/efcLzFy1axCOPPBLtYpwgK9HICzdPRhdOnjqtGl24Kk+nOZpKdeFqvKPLQ1V6iqLg8QcjO3S724fd7ccWnkfuNzUvDy2zuf1o1JASbyDFpAvN43Ukx+tJjdeHG0vqI9VosfwSBoMKTb4AJr0m6jUAOk0oaRemJwAt/zecHj9xOk2POMIRQnROlsXI7ecN5EczCimusuNw+ylMTyAlXh/rovVaBq2GzEQNmYl95xL7mJ9EXrhwIQsWLIjct1qt9OvXD5vNFvX3mpB9qj9c8/mwACjhWQC8hKZjGQGjETKMGkADRON6dwWCHpyOjjem7Aqn0aaz03rIRxdCRFGOCTBpIeDG1o6u7kXv1rzfbs8JlaiHj7S0NDQaDVVVVS2WV1VVkZWVdcLzDQYDBsPRHXhz4fPz86NdNCGEEEJ0MbvdjsXSeu/CUQ8fer2eiRMnsmLFikibj2AwyIoVK5g/f36br8/JyaG0tBSz2Rz1qv/m9iSlpaVyGe8pyDZqnWyftsk2aptso7bJNmpdT9w+iqJgt9vJyclp87ldctplwYIFzJs3j0mTJjFlyhQWL16M0+nk5ptvbvO1arWavLy8rihWRGJiYo/5Y/VUso1aJ9unbbKN2ibbqG2yjVrX07ZPWzUezbokfNxwww3U1NTw4IMPUllZybhx41i2bNkJjVCFEEIIcebpsgan8+fPb9dpFiGEEEKcWc6oThQMBgMPPfRQiwauoiXZRq2T7dM22UZtk23UNtlGrevt26fHDSwnhBBCiL7tjKr5EEIIIUTsSfgQQgghRLeS8CGEEEKIbhXz7tWPFwwGKS8v75JOxoQQQgjRNY7tZEytbr1uo8eFj/LyculaXQghhOilSktL2+wstMeFD7PZDNCjuowVQvRc9U4viz/dQ53Tw++vGYPZqIt1kYQ4IzV3+d68H29NjwsfzadaelqXsUKInmfZ9kp+9e42ah2h8ac/3G3lthmFMS6VEGe29jSZkAanQohep8Hp5cevbeKOl4uodXhJNoVqO174qgR/IBjj0gkh2iLhQwjRq3y8o5ILn1jDki3lqFVw18yBrL5/Finxeo40NvHpzqpYF1EI0QYJH0KIXqHB6eUn/9nE7S8VUevwMDgjgXfums79Fw8j0ajje1P7AfCvLw/GuKRCiLZI+BBC9HifhGs73tscqu24c+ZAlt5zDmPzkyLPuems/ug0KjaUNLC1rDFmZRVCtE3ChxCix2p0ebn3P5v4Ubi2Y1BGAm/fNZ1fXDwMo07T4rmZiUYuH5MDwPNflsSgtEKI9pLwIYTokT7dWcWFT6zh3XBtxx3nDeT9e85h3DG1Hce7ZXoBAO9vLafK5u6mkgohOkrChxCiR2l0ebnv9c3c9uJGauweBqbH8987z+aBS06s7Tje6DwLkwck4wsovLzuUDeVWAjRURI+hBA9xvKdVcx5Yg3vbDqCWgW3n1fIBz8+l/H9ktu9jubaj1fWH8btC3RVUYUQndCh8LFo0SImT56M2WwmIyODq6++muLi4hbPmTlzJiqVqsV0xx13RLXQQoi+Z9n2Cn744kaqj6ntWHjJ8DZrO4534YhMcpPiqHd6eW/zkS4qrRCiMzoUPlavXs3dd9/NunXr+PTTT/H5fMyZMwen09niebfddhsVFRWR6bHHHotqoYUQfYuiKCxevheAayfmdbi241hajZofnD0AgH99UYKiKNEqphAiSjrUvfqyZcta3H/hhRfIyMigqKiIGTNmRJabTCaysrKiU0IhRJ+3ek8NuyvtxOs1/PqyER2u7Tje9ZPzeWL5Hoqr7Hy1v47pg9KiVFIhRDR0qs2H1WoFICUlpcXyV155hbS0NEaNGsXChQtxuVynXIfH48Fms7WYhBBnlmdX7wfgu1P6YTF1fmA4S5yO6yaGRtV8XjodE6LHOe3wEQwGuffee5k+fTqjRo2KLL/xxht5+eWXWbVqFQsXLuSll17ipptuOuV6Fi1ahMViiUz5+fmnWyQhRC+0ubSRdQfq0apV3HpuQdTW+4Nww9MVu6s5WOts49lCiO6kUk7zhOidd97JRx99xBdffEFeXt4pn7dy5UouuOAC9u3bx8CBA0943OPx4PF4Ivebh+S1Wq0yqq0QZ4A7Xy7io+2VXDMhjz9dPzaq6771hQ2s2F3ND84ewMNXjozquoUQLdlsNiwWS7v236dV8zF//nzef/99Vq1a1WrwAJg6dSoA+/btO+njBoOBxMTEFpMQ4sxwoMbBsh2VQOiy2mi75ZxQ7ccbG0uxNvmivn4hxOnpUPhQFIX58+fzzjvvsHLlSgoK2q4i3bx5MwDZ2dmnVUAhRN/13OcHURS4YFgGQzLNUV//2QNTGZppxuUN8ObG0qivXwhxejoUPu6++25efvllXn31VcxmM5WVlVRWVtLU1ATA/v37+c1vfkNRURElJSUsWbKEuXPnMmPGDMaMGdMlH0AI0TtV293895syAO6YeeIp2WhQqVTcPH0AEBrvxR8Idsn7CCE6pkPh45lnnsFqtTJz5kyys7Mj0+uvvw6AXq9n+fLlzJkzh2HDhvHTn/6Ua665hqVLl3ZJ4YUQvdfzX5bg9QeZ2D+ZyQNS2n7Babp6fC7JJh1HGptYvquqy95HCNF+Herno622qfn5+axevbpTBRJC9H12ty8y9srtM6Lf1uNYRp2G703tz1Or9vGvL0q4eJScAhYi1mRsFyFEt3vt68PY3X4Gpscze3hml7/f96f1R6tW8XVJPdvKrF3+fkKI1kn4EEJ0K68/yD+/CHX8dfuMgajVqi5/z8xEI5ePCdV4SKdjQsSehA8hRLd6d/MRqmweMhMNXDU+p9vet/my26Vby6m2ubvtfYUQJ5LwIYToNsGgwt/CXanfek4BBm3nxnDpiDF5SUzqn4wvoETamwghYkPChxCi26zYXc3+Gidmo5bvTunX7e/fXPvxyvrDuH2Bbn9/IUSIhA8hRLdpHkDuprP6YzZ2fgC5jpozIpPcpDjqnF6WbCnv9vcXQoRI+BBCdIuNJfUUHWpAr1Fz89kDYlIGrUbNvLP7A/CvLw622X2AEKJrSPgQQnSL5lqPaybmkpFojFk5bpjUD5New+5KO2sP1MWsHEKcySR8CCG63J4qO8t3VaNSwW3ndm2nYm2xmHRcOzE0IOa/viiJaVmEOFNJ+BBCdLm/rzkAwEUjsihMT4hxaeAH4dM+K3ZXUVLrjG1hhDgDSfgQQnSpCmsT720+AsDt58W21qNZYXoC5w/LQFHgha9KYl0cIc44Ej6EEF3qX18cxBdQmFqQwvh+ybEuTsQt00OX3b65sRSb2xfj0ghxZpHwIYToMlaXj1fXHwbgjpkDY1yalqYPSmVIZgJOb4A/flxMrcMT6yIJccaQ8CGE6DIvrz+E0xtgWJaZmUPSY12cFlQqFbeGOx17ce0hpv7vCr7/z/W8vuEwVpfUhAjRlbSxLoAQom9y+wKRQdxuP68QlarrB5DrqOsn5eP1B3mzqIytZVY+31vL53tr+dW72zl3cDpXjM1m9vDMmHSIJkRfJuFDCNEl/vtNGbUOL7lJcVw+pvsGkOsIlUrF96cN4PvTBlBS6+SDbRUs3VLO7ko7K3dXs3J3NQatmllDM7hibA7nD8sgTt9949EI0VeplB7WxZ/NZsNisWC1WklMTIx1cYQQpyEQVLjgT59RUufiwctHRMZU6S32VtlZurWC97eWc6Dm6KW4Jr2G2cMzuXxMNucNTe/WgfGE6Ok6sv+W8CGEiLoPt1Vw1yvfkGTS8dUD52PS985KVkVR2Flh4/2toRqRsoamyGNmo5aLRmbx7Qm5nFWQilrd804rCdGdJHwIIWJGURSuevpLtpZZ+fH5g1gwZ2isixQViqKwpczK+1vKeX9rBZU2d+Sx3KQ4rpmYxzUTcumfGh/DUgoROxI+hBCnxe728eW+Wlburuabw40YdWqSTfrwpCM5PnQ7yaQj5bjbcToNKpWKr/bXcuNz6zHq1Hz5i/NJTTDE+mNFXTCosPFQA+9uPsLSLeXY3f7IY1MGpHDtxDwuHZNNgqHzNT52t4/1B+r5cn8tX+2ro8ru5upxufxoRiE5SXGdXr8Q0SLhQwjRLoqisL/GwardNazcXc2Gknr8wdP7SdBr1SSbdHj8QRpdPuZO68+jV42Kcol7HrcvwCc7q3irqIzP99bQ/Isap9NwyagsrpmYx7TC9p+WcfsCfHOoIRQ29textcxK4CR/E61axbfG53LHzIEM7MIu673+IKUNLvqnmNBqpHcGcWoSPoQQp+T2BVi7v45VxdWsKq6mtL6pxeOFafHMGpbB9EGpqFBR7/TS4PLS6PJR7/LS6PLS4PTR4PKGJx9ef7DFOvRaNcvvO49+qabu/GgxV2Ft4p1NR3irqKxFQ9XcpDi+PSGXaybkMSCt5WmZQFBh2xErX+6r5av9tWwsacBz3PYckGri7EFpnD0wlXiDlufWHOCr/aEReVUquGRUFnfNHMSoXEtUPkcwqLChpJ73tpTz4bYKGl0+Eo1azh2Szswh6Zw3NJ0Mc+xGJhY9k4QPIUQLpfUuPisOXTr61f66Fjs3vVbNWYWpzBqazqyhGSfsHNuiKApNvgD1znBAcXrJSTIyKMMc7Y/RayiKwubSRt4qKmPJcadlJvVP5tsT8vD6A3y5v451B+paPA6QbjYwfWBqJHDkJZ8Y4jYdbuCvn+3n051VkWXnDUnn7lmDmFKQclpl3lVh573NR1iypZwK69E2LRq16oTal1G5icwamsHMoemMy09G040Nbm1uH+WNTVQ0uim3tpxXWJvQadRM6JfMpAHJTBqQwoBUU4/sZ8bh8XOgxoFJryUnydhrG2Y3k/AhRIw1/6gcqHGyPzw/WOtEpYLMRCOZiQYyzMbI7cxEIxmJBlLjDR3+EQ8EFRpdXuqcXmodHmodXuocHuocoftFhxrYW+1o8Zoci5FZwzKYNTSDswel9vofvZ7M7QuwfFfotMyaPTWc7KyW2ajlrMJUpg9MZfqgNAZlJLR7Z1lcaeeZz/axZEt5ZN2T+idz96xBzBya3uZ6Dte5WLLlCO9tLm/xf2I2aLl4VBZXjctlSkEK245YWV1czariGrYdsbZYR5JJx7mD05k1NJ0ZQ9JJO812Pv5AkHqXlzpHaDo2UJRb3VQ0NlFhdePw+Nte2THSEvRM7J/M5AEpTOyfzMgcC3pt951C8vgD7K92sqfKTnGVnT2VdnZX2jnS2LLW0RKnIycpjhyLkewkY/h2HNmW0O3MRGO3lrujJHwIEeb1B7E2+SKTrXnu9qFRqzAbdZgNWhKMWhIMocls1BJv0KJr4/x2IKhwpKGJ/TWOUMCodUYCR7X99MYJ0ahVpCcYIoGkOZxYTHpsTT5qjwkVdQ4vdU4P9U7vSXdox693Yv9kZg3N4PxhGQzJbP/OTURPlc3NO5uO8NG2ChKMWs4emMb0QWmMyknsdHuKw3Uu/rZmP29uLMMbCNVsDc9O5K6ZA7l0dHaLUFtj9/DB1nLe21LOpsONkeV6rZoLhmVw1bgcZg7NwKg7eT8mNXYPq/fU8FlxNWv21GA7puZGpYLRuRZmhmtF+qWYqA8H43pnOFg4jwbkeqeX2vD/cWMHurVPMunIthzdUWdb4sgJzx1uPxsPNbCxpJ6tZdbI9mhm1KkZm5cUCiMDkpnQLxlLXOd7sQ0EFQ7XuyiutFNcaY+EjYO1zpO22wFISzDg8QWwtyNQqVSQnmAgOymO3CQjySY9gaCCNxDEF1Dw+YP4AsHw/fCyQBCvv+V9XyBIvxQTb981vdOf+Vg9Inw8/fTTPP7441RWVjJ27FiefPJJpkyZ0ubrJHx0XvOftCt3LoqiYGvyU+PwhI+2Pbg8ATRqFVqNCp1GjVYdnmtUaNQnLtOqVWjVoduBYPOXRDn65fGH5qEvztHlR79IQZq8Jw8XzVOTL3Dan9GoU5Ng0GE+JpgkGLWogJI6JyV1rhPaOhwrLcFAYXo8A9PjGZieQEFaPGqViiqbmyqbhyq7m+rm2zY3tQ5PmyGiNckmHakJBlLj9aQlGEhL0JOaYGBgegLnDE6Lyo+r6PmqbG7++cVBXlkXGlcHoCAtnttnFKLTqHlvSzlf7quN7AzVKjh7YBpXjsvhopFZHf4/8QeCbC5tZFVxNZ8V17Cj3Nap8qtUkGLSkxKvJ8tiDB35J4XmOUlx4aDR/lMUbl+A7UeskTCy8VDDCSFHpYKhmWbG90vGbNTiDygEFQV/MEggGGoD4w82L1PC98OPhZfVOz3srXKc0F6nWaJRy7CsRIZkJTA008yQTDNDs8wkmfRA6FRSi9NIjU0tTylZ3a3+3nRUQVo8q342M2rrgx4QPl5//XXmzp3Ls88+y9SpU1m8eDFvvvkmxcXFZGRktPrargofiqLgCzTvlEPLmnfNKpXqmNtHl53s9R5/AK8/iCc8hW4HWt72tXwMlYo4nQajTh2eN09H7zfPDVp1i1bxJzuf3uDyhuc+Gpxe6l1eGo67HwwqJMbpSDRqSYzTYYnTkWjUhZbFaUk0hpeFn9N822zQYm3yhUOFl1r70XBRGz7iDi3znnA00ZOZw5/Rcsy2CCgKDrcfhyc02d1+HB4fbl/7P5deq6YgNZ6BGfEUpiVQmB5PYThonM6PeJ3TS6XVHQoodk84nLhpdPlIOiFcGEhN0JOaoCfFpJcrEUQLjS4v//7qEM9/dfCkNQpj85O4amwOl4/JJiMxeo1Hq21uPgvXiny+pxa7xx/6343Xkxof+p9NiQ8F47Tm2+HlqfF6kkz6Lm0/EgwqHKh1sLGkgQ0lDRQdqqekzhW19Rt1agZnhMLFsCwzQ7LMDM00k5lo6NQBoaIo1Dm9oVASPhXV6PKh16rRaUIHcjqtGn344K950mtb3teFH4/TaTrcvqstMQ8fU6dOZfLkyTz11FMABINB8vPzueeee3jggQdafW1XhY/9NQ4u+NPq03qtSgXdeXLKoFUTp9eg06ixNflOmaR7ArNRS3p4R5hg1OIPKvgDQfwBBV8wPA8Ejy4PKvgDoaMGXyC0zBdU0KpV4S+RGr1GHflCHf0ChZbrNC2fZ9BpSIxrGSyOn8xGXYd+zHyBIM5IGAlPbj/28NwfDFVZDkxPICcprlsb2gnRUU6Pn9e+PszL6w6h1ai5YkwOV47LoSDKO56TCYRrC9o6hRlr1XY33xxqYGuZFX9QQaNWoVGFamxbTK0sSzBqGZppJj/FdMb+JnRk/x31VmZer5eioiIWLlwYWaZWq5k9ezZr16494fkejweP5+j5cZutc1V2XeFkwUOvUWPQqjHoju4EQ/PQcr1WjUEbWqag4PYFafIF8PgCNPkCkfvu8NRcKwNEalWOf7/keB3J4erIUGdPOlJModsp4SOG0H0dWrUam7tlGwdbk/+Edg+RZeHn2j1+zAYtaeZQoEg3G8Lh4ujRduix0P1TnRPuzXQaNUkmfaQ6VIjeLN6g5YfnFvLDcwu7/b01ahUaev6OOMNs5OJR2Vw8KjvWRTljRD181NbWEggEyMzMbLE8MzOT3bt3n/D8RYsW8cgjj0S7GCcYkBrPlofmQHgfr4RvKJH74Xl4wdH7R9cRChShsBHtcRz8gSBufxC3L0CTNxA5lZNoDPUeadJrOlxll2XpeFWqoijSEFEIIUSXivn1dQsXLmTBggWR+1arlX79+nVJDYjqFLfbzQdeH3ijVJ6T0QN6LeG/jArwE/D4Oc2LJ4QQQohu0bzfbk9rjqiHj7S0NDQaDVVVVS2WV1VVkZWVdcLzDQYDBsPRa8KbC5+fnx/togkhhBCii9ntdiyW1nvbjXr40Ov1TJw4kRUrVnD11VcDoQanK1asYP78+W2+Picnh9LSUsxmc9Sr/202G/n5+ZSWlsplvKcg26h1sn3aJtuobbKN2ibbqHU9cfsoioLdbicnJ6fN53bJaZcFCxYwb948Jk2axJQpU1i8eDFOp5Obb765zdeq1Wry8vK6olgRiYmJPeaP1VPJNmqdbJ+2yTZqm2yjtsk2al1P2z5t1Xg065LwccMNN1BTU8ODDz5IZWUl48aNY9myZSc0QhVCCCHEmafLGpzOnz+/XadZhBBCCHFm6dk9v0SZwWDgoYceatHAVbQk26h1sn3aJtuobbKN2ibbqHW9ffv0uIHlhBBCCNG3nVE1H0IIIYSIPQkfQgghhOhWHW5wumbNGh5//HGKioqoqKjgnXfeifTnAaHrfB966CGee+45GhsbmT59Os888wyDBw9u1/qDwSDl5eVd0s+HEEIIIbrGsf18qNWt1210OHw4nU7Gjh3LLbfcwre//e0THn/sscf4y1/+wr///W8KCgr49a9/zUUXXcTOnTsxGtsea6S8vFx6NxVCCCF6qdLS0jb76+pUg1OVStWi5kNRFHJycvjpT3/Kz372MyA0VktmZiYvvPAC3/nOd9pcp9VqJSkpqUf12iaEOLVgUKHG7qHc2kSF1U15YxP9UkzMGXnicApCiL6rudfVxsbG7u1e/eDBg1RWVjJ79uzIMovFwtSpU1m7dm27wkfzqZae1mubEGcqjz9ARaObI41NHGlooqyxifLw7SONTVRYm/AFTjyG+aQwmyGZ5hiUWAgRS+1pMhHV8FFZWQlwQk+mmZmZkceO5/F48HiODtnaFaPZCiE6ZneljQff28HBWic17RhSWaNWkZVoJDc5jkqrm8P1Lt7fUs6COUO7obRCiN6my3o4ba9FixbxyCOPxLoYQohjPLlyH18frI/cj9NpyE2OIycpjtykOPKSQ/PmZZlmA1pNqIHZu5uOcO/rm3l/awX3XThEGo4LIU4Q1fCRlRU6x1tVVUV2dnZkeVVVFePGjTvpaxYuXMiCBQsi95vPGQkhYsPh8bN8ZxUAz940kSkFKSSbdO0OERcMz0CvVXOg1snOChsjc9o30JQQ4swR1X4+CgoKyMrKYsWKFZFlNpuN9evXM23atJO+xmAwRNp3SDsPIWLv4+2VePxBCtPjuWhkJinx+g7VXpiNOmYNTQfgg60VXVVMIUQv1uHw4XA42Lx5M5s3bwZCjUw3b97M4cOHUalU3Hvvvfz2t79lyZIlbNu2jblz55KTk9OiLxAhRM/13pZyAK4am3vap0wuH5MDwPtbK5ARHIQQx+vwaZeNGzcya9asyP3mUybz5s3jhRde4P7778fpdPKjH/2IxsZGzjnnHJYtW9auPj6EELFVY/fw5b5aAK4al3Pa67lgeAZGnZrD9S62HbEyJi8pSiUUQvQFHQ4fM2fObPVIRqVS8eijj/Loo492qmBCiO73wdZyAkGFsflJDEiLP+31mPRaLhiWyQfbKnh/a4WEDyFECzK2ixAi4ugpl9Ov9Wh2+ZhQo/MP5NSLEOI4Ej6EEAAcrnOx6XAjahVcPja77Re0YdawDOL1Go40NrGptLHzBRRC9BkSPoQQALy3+QgA0welkWHufBsto07D7BGhDgff3yJXvQghjpLwIYRAURTeDYePK6NwyqXZZaNDNSgfbqsgGJRTL0KIEAkfQgh2VtjYX+NEr1Vz8ajoDQh33tB0zAYtlTY3RYcborZeIUTvJuFDCMF7m0MNTWcPz8Bs1EVtvQathgtHNp96KY/aeoUQvZuEDyHOcMGgwpJw+LhybG7U139FuMOxD7dXEpBTL0IIJHwIccZbf7CeSpsbs1HLrGHpUV//9EFpWOJ01Ng9rD9YF/X1CyF6HwkfQpzhlmwJNTS9dFQ2Bq0m6uvXa9VcFD71ImO9CCFAwocQZzSPP8CH2yqBznWn3pbmsV6Wba/EHwh22fsIIXoHCR9CnMFWF9dgbfKRmWhgamFql73P2QNTSTbpqHN6WXtATr0IcaaT8CHEGay5O/UrxuSgUZ/eCLbtodWouXhUqM8P6XBMCCHhQ4gzlMPjZ/nOKgCuGhf9q1yOd0V4rJdlOyrxyakXIc5oEj6EOEN9vL0Sjz9IYXo8o3ITu/z9phamkpagx9rk44t9tV3+fkKInkvChxBnqKMj2OaiUnXdKZdmGrWKS+TUS6dsK7Py5Iq9fLqzCrvbF+viCHHatLEugBCi+9XYPXwZrn3oyqtcjnf5mGxeWneIT3ZW4vGP6pJLe7vLltJGNpTU850p/UgwdO1PqaIo/POLg/z+o934wx21adUqxvdL4tzB6ZwzOI0xuRa0GjmeFL2DhA8hzkAfbC0nEFQYm5/EgLT4bnvfyQNSyEw0UGXz8Pme2siot73N+1vLWfD6FryBIC+uPcSfrh/L5AEpXfJe1iYf97+1hY93hNrnTClIodrmpqTOxYaSBjaUNPDnT/eQaNRy9sA0zh2SxrmD0umXauqS8ggRDRI+hDgDHT3l0n21HgBqtYpLR2fz/JclvL+1vFeGj39/VcLDS3egKGDUqTlc7+L6v63l9hkDue/CwVGtzdl+xMpdr3zD4XoXOo2KX18+gu+f1R+VSkVpvYvP99by+d4avtxXi83tZ9mOSpbtCPXb0j/VxDmD0jh3cDrTBqZiiTv5mD3BoILD68fq8mFt8mFz+7A1+bA1+SP3NWoVN07tR4bZGLXPJs5sKkVRetRgCzabDYvFgtVqJTGx6xvBCXGmOVznYsbjq1CrYN0vL+j2HUrRoXqueWYt8XoNRb++EKOud5x6URSFP32yh6dW7QPg+2f152dzhvKbD3byVlEZAMOyzDxxwziGZ3fut0tRFF79+jCPLN2J1x8kNymOv35vAmPzk076/EBQYWtZI1/sreXzvbV8c7ghcnoGQK2CcflJZCYaI4HCGg4YdreP9gy5k5sUx4u3TmFgekKnPpvouzqy/5bwIcQZ5skVe/nTp3s4d3AaL906tdvfPxhUOOcPKym3unn2pgmR/j96Mn8gyP+8s53XN5YCsODCIdxz/qBIQ92Pd1Tyy7e3Uef0otOoWHDhUH40o/C0+k5xevz8zzvbeDc82N8FwzL40/VjSTLp270Oh8fPuv11fLGvljV7azhQ42zzNQatmsQ4HZY4HYlGbWgevr9mTw0ldS6STTr+9YPJjO+X3OHPJfo+CR9CiJNSFIXZf17N/honj187husm5cekHL/7YCfPfX6Qy8dk89SNE2JShvZq8ga457VvWL6rGrUKfvet0Xx3Sr8Tnlfr8PDAf7exfFeobcak/sn8+fpxHWp7sbfKzp2vfMO+agcatYqfXzSUH51biLqTHcAdaWziq321NPkC4XDRHCy0JIbvt1YDVefwcMsLG9hSZsWoU/PX703g/GG975SZ6FoSPoQQJ7X9iJXLn/wCvVZN0a9mYzaevB1AV9tS2shVT39JnE5D0a9nY9L3zOZnjS4vt/57I0WHGjBo1Tz53fHMGZl1yucrisKbRWU8unQnDo8fk17Dry8fwXcm57d5OfM7m8r45dvbafIFyDAbePK747u0y/uOcnr83P3qN3xWXINGrWLRt0Zz/eTYhFfRM3Vk/y3XZQlxBlkSbmg6e3hGzIIHwJg8C/kpcTT5AqzcXR2zcrSmvLGJ655dS9GhBhKNWl7+4dRWgweASqXi+kn5fPSTc5lakILLG2Dh29u49d8bqba7T/oaty/0nPte30KTL8D0Qal8+JNze1TwAIg3aHlu7iSumZBHIKhw/3+38tTKvfSw41fRS0j4EOIMEQwqLAm3I7hybNd3p94alUrFZaNDV9r0xA7H9lbZueaZr9hb7SAr0cibd5zdoUtp81NMvHbbWfzqsuHotWpW7q7moifW8OG2lp/1UJ2Ta575ite+PoxKBT++YDAv3jKVtARDtD9SVOg0av543RjumjkQgD9+socH39tBoD0tVoU4hoQPIc4Q6w/WU2lzYzZqmTUsPdbF4fLwWC+riqtxePwxLs1RRYfqufbZtVRY3QxMj+e/d53N0Cxzh9ejVqv44bmFLJ1/DiOyE2lw+bjrlW+47/XNWJt8LNteyeVPfsGOchsp8Xr+ffMUFlw4pEsH+IsGlUrF/RcP4+ErRqBSwUvrDnH3K9/g9gViXTTRi/TME61CiKhbsuUIAJeOyu4RPYuOzEmkIC2eg7VOVuyqOu3B7UpqnTyydAe7K+2MybMwtSCVqYUpDM9K7HBDzeU7q5j/2je4fUHG90viX/Mmkxzf/qtMTmZolpl3757OX1bs5a+f7eOdTUdYVVxNoyvUPfrE/sk8deN4si1xnXqf7vaD6QWkm43c9/pmlu2oZO6/vua5uZNO2Z+IEMeS8CFEDxcIKhysdVCYlnDaVz14/AE+3BbqfKo7u1NvTejUSzZPrdrH0i0VHQ4f/kCQf3xxkCc+3YPHHxolt8LqjvQEmmjUMqUgJRJGRmQnttr9+BsbSln4zjYCQYXzh2Xw1I3jo9YQVq9V87OLhjJrWAY/fWMzJXUuAG47t4D7Lx6Grpd2i37ZmGxS4vX86MWNfH2wnuufXcsLt0zudUFKdD+52kWIHqzB6eWOl4tYf7CekTmJ/PyioZw3JL3DA8F9sqOSH71URGaiga8euKDHVO3vrrRx8eLP0WvUbPjV7HYfNe8ot/KL/25l+xEbANMHpfKjGQPZUW5l/YF6NpbU4/S2PA2QYNAyaUByJIyMzrWg06hRFIW/frafxz8uBuCaCXn8/prRXRYIXF4/L609xPDsRGYMif3pr2jYVWHjB89/TZXNQ47FyL9vmcLgzI6fqhK9m1xqK0QfsLfKzq3/3sjheleL5VMKUvjFxUOZ2L/9DSDvfvUbPthawQ/PKeBXl4+IdlFPm6IoXPjEGvZVO/jTdWO5ZmJeq893+wL8ZcVe/rbmAIGgQqJRy68uH8F1E/NaBDJ/IMiOchvrD9ax/kA9X5fUY3e3bFdi0muY2D+ZBIOWj7aHaoXunDmQ+y8a2i2j/PY1ZQ0u5v3ra/bXOLHE6fjnvElM6qLxbkTPJOFDiF5uVXE1P351E3aPn7zkOP503Vg+3VnFi+sO4Q2fYrhgWAY/u2hom115Ozx+Jv7mUzz+IO/fcw6jci3d8RHabfHyPSxevpdZQ9N5/uYpp3ze+gN1LHx7GwdqQ711Xjo6i4evHNmu7uEDQYVdFTbWH6xn3YE6NpTUR9pcNPv15SO49ZyCzn2YM1yD08ut/97AN4cb29UviuhbJHwI0Us1D53+vx/uIqjAlAEpPHPTBFLDl16WNzbxlxV7ebOojEBQQaWCK8fmsODCIfRPPfnotP8tKuOnb26hMD2eFQvO63FH9fuq7cz+8xq0ahUbfzX7hG7E7W4fv/9oN6+sPwxAhtnAb64exUWd2KkFgwp7qu2sP1DPltJG5ozM4uJRspOMhuN7hL139hDmjMxkSIa50z21ip5NwocQvZDXH+TX7x4dP+SGSfn85upR6LUntj04UOPgT5/u4YOtoX4jtGoVN0zO58cXDCYzsWVNwNx/fc2aPTXcN3sIP5k9uOs/yGm4ePEadlfa+cM1o7lh8tGuy5fvrOJX726n0hbqoOu7U/J54JLhckVFD+cPBPnVu9v5z4bSyLJEo5ZJA1KYNCCZKQNSGJ1n6RFXXYnokfAhRC9TH25Y+vXBetQq+OWlw7n1nII2aym2H7Hy+MfFrN5TA4SGeJ939gDuPG8gSSY9NXYPZy1aQSCo8NnPZjIg7eS1I7H21Mq9/PGTo4Pd1To8PLxkB++Hw9WAVBOLvj2GaQN7Vq+f4tQUReG1r0v5aHsFRYcacB3XAFivVTM2z8LkASlMHpDChP7JHQqVTd4ARxqbONLYRHljE0caQvOyxqYT2vcc/y069mt17G2NWs3sYRnMmz6AxBj2ANwaRVHYUW7jk51VHGloYkx4Gw7Lin3NkoQPIaLM6w+yo9zKhpJ6qmwezh+WwbTC1Kh82fdU2bn13xsorW/CbNDylxvHM2toRofWsf5AHY99XEzRoQYAzAZtaFRVjYrHlhUzNj+J9+6e3umydpWDtU5m/fEzNGoVD14+gieW76HR5UOjVvHDcwu4b/aQVgc+Ez2bPxBkV4Wdr0tCVyJtKKmn1uFt8RyVCoZmmkNhpCCFMbkW7G5/JGA0h4vm+/VO7ynerfPMRi03Ty/g1ukFWEyxDyH+QJCvS+r5ZEcVn+6s4khj0wnPMRu1TOqfzOSCFKYWpDA6N+mktaZdScKHEJ3k9Pj55nADGw7Ws6GkgU2lDbh9wRbPyU2K45oJuVw7Mb9DI5cea+XuKn782mYcHj/9Ukz8c96k075EUVEUVu6u5vGPi9ldaW/x2IOXj+CWHt6Y8rK/fM6Oclvk/ojsRB67dkyPayArOk9RFErqXOHvVz0bDzVwMNyQuCMSDFpyk+LITY4jJ8lIbpKJ3OQ4kuJ0kRqN4/dwx949fvdXbfPw3OcH2FvtiKx/3tn9ufWcQlI62dlcR7m8ftbsqeWTnZWs3F3dooG0UafmvCHpDM00s6m0kW8ONZxwablBq2ZcfhJTCo7WLCUYurZrLwkfQnRQrcPDxpJ6vj7YwMZD9ewot50wXkWyScekASkkm3Qs216J7Ziq3akFKVw7MY9LR2cT344vuKIoPPf5ARZ9tBtFCb3+mZsmRuUHLhhUWLq1nD9/uodDdS60ahVfLTy/XVeFxNJzaw7wuw93YdCquXf2EH54bkGv7XxLdFy13U1RSUO4dqSBXRU2kuP1kXCRmxSacsLz3OQ4Eo3aqDegDgYVlu2o5C8r9kZCvEmv4fvT+nPbuYVdOu5OncPDit3VfLKjis/31kQ6z4PQ78/s4ZnMGZnFOYPSiNMfrQk8tmapOdDVHVczpFGrGJGdyOQBKUwpSGbSgJSofxYJH6fg9PjbtWM4kyiKgs3tp9HlpcHlw9bkI96gJSVeT0q8Pqpf7kBQoc7hodLmpsoWmlfb3FTbPAQVBa1GhVatRqNWodOo0KjV4bkKrVqFVqMOzdUqNBo1OnX4MY0Ktar5taHztpG5KvSc5kkbngPsrrRHvqgHTnLUlZsUFzlqmFKQ3KKHUbcvwCc7q3irqIzP99ZEjq5Meg2Xjc7m2ol5TClIOem28/gD/Oqd7bxZVAaEGlE+cuXJG5Z2hi8Q5KPtlaTF6zl7UFpU190VAkGFpVvKGZef1GPbpojuoyhKTK/MCgYVlu+q4i8r90Y6szPq1Hxvan9un1FIRmLnw3wwqHCo3sWKXVV8srOKjSX1HHvMk58Sx5wRWcwZkcnE/smt9tB7LEVR2F/jZEM4jHxdUk9ZQ8tTNWaDli0PzYlqO5EeET6efvppHn/8cSorKxk7dixPPvkkU6ac+hr+Zl0VPhpdXsY9+im5SXEMzkxgSKaZwRkJDA7PoxFK3L4AB2qc7KtxsK/Kzt5qB/uqHSjAgNR4CtJM9E+NpyAtngFp8WQnGqP6h1cUBYfHT7XdQ53DS4PLGwkVDS4vDc7Q7eZljS4vjS4f/lZGpNSqVSTH60kx6SOBJDleR0q8gRSTjpQEQ+QxjVpFtd1NpdVNtd1DpdVNla158lDj8PTo0S+HZpqZXJAcaQCXk9S+LqIrrE28/c0R3ioqa1F13D/VxLUT8vj2xDxyw+uqc3i44+UiNpQ0oFaF+pb4wdkDetzlr0KIEEVRWFVczf+t2MeW0kYg1Fj2xin9uP28wnZ1Ja8oCjV2D8VVdoor7eytclBcZWdvlf2E0yUjcxJDgWNkJsOyzFH7baiwNvF1+GBrw8EGcpKMrfarczpiHj5ef/115s6dy7PPPsvUqVNZvHgxb775JsXFxWRktN6QrqvCR9Gheq55Zu0pH89NimNIcyjJNDMkM4FBGQknHdvB6fGzv8bB3ipHJGDsq7ZzuN5FR/atBq2a/qmmcDAJBZLm25mJhsg/XSCoUOf0UGP3UG33UBPekVfbQjv5yHK7h6bTHFnSpNeQbNJjNmpxev00OH1dMtKoWgXpZgOZicbwZCDDbESrUeEPKPiDCv5AkEBQwRdQCASD+IIKgebHgsETnhNUFPwBhYCiEAieZDrJcn9QoV9KHJMLUpjcP3T53/H9S3SUoigUHWrgzY1lvL+1PPKjolLB9IFpXDQqi7+t3k9ZQxNmo5anbpzAeX2ke20h+jpFUVizt5a/rNgbadit16i5fnIed5w3kLzkULuvRpeXPeFwsafSHppX2U/o1K6ZTqNiYv9kLhqZxYUjMiPr6Wr+QLDdNSntFfPwMXXqVCZPnsxTTz0FQDAYJD8/n3vuuYcHHnig1dd25WmX5n+KPeHEuafKwd5q+wmtrpupVJCXHMeQDDM5SXEcrnexr9px0pbGzRKNWoZkmhmUkRCZ1CoVJXVODtY6Kal1UlLnorTe1WqNQ5xOQ06SEbvbT53T26EaA7NBS2qCnuR4PckmPUkmHckmPckm3UmWhW6f7EoCty9Ag8tLvbPl1OD0UucM1aw017DUO734gwqZZiMZiQaymsOFxUhmOGxkWYykxuuj/g/fE7m8fpZtr+TNjWWsPVDX4rEBqSb+MW8ygzISYlQ6IcTpUhSFtfvr+L8Ve1l/sB4I1RBP6JfMoXonVTbPSV+nVoVqwIdkmhmSZWZoppmhWQn0T43vM22bYho+vF4vJpOJt956i6uvvjqyfN68eTQ2NvLee++1eL7H48HjOfrHstls5Ofnd2uD03qnNxRIqh3hUBKqFju+wc6x0hL0DMpIYHCGmcGZCQxKT2BQZgLpCYZ2VZP5A0HKGpo4WBcOJLVODta5KKl1UtZwYg2KSgWp8QbSzQYymqdEA+kJBjISjWSYmx8ztmiIJGKvtN7Ff78pY8mWcgakxvPn68d2upZFCBF76w7U8eTKvXy5r+UBRqQmPRwymg9I+/rl4h0JH1FvfVlbW0sgECAzM7PF8szMTHbv3n3C8xctWsQjjzwS7WJ0SEq8nrMKUzmrsGUHRnUOT6R2pLzRTb8UUyRoJHfyqgStRh06zZIWD0NbPub1ByltcFFpdWOJ05FuNpwxNQZ9UX6KiXtnD+He2UNiXRQhRBQ17zc2HW5gX7WDwvQEhmQmYO6hHZT1JDG/9GPhwoUsWLAgct9qtdKvXz9sNlsrr+oeOmBkuo6R6ceNzBhwYwt399xV0g2QntEccLy4urBDHSGEEKdvYJKGgUmh/mgUbxM276lPzfdlzfvt9pxQiXr4SEtLQ6PRUFVV1WJ5VVUVWVknDtxkMBgwGI5ea9xc+Pz8/GgXTQghhBBdzG63Y7G03jlg1MOHXq9n4sSJrFixItLmIxgMsmLFCubPn9/m63NycigtLcVsjt4lRs2a25OUlpZKB2anINuodbJ92ibbqG2yjdom26h1PXH7KIqC3W4nJyenzed2yWmXBQsWMG/ePCZNmsSUKVNYvHgxTqeTm2++uc3XqtVq8vLyuqJYEYmJiT3mj9VTyTZqnWyftsk2aptso7bJNmpdT9s+bdV4NOuS8HHDDTdQU1PDgw8+SGVlJePGjWPZsmUnNEIVQgghxJmnyxqczp8/v12nWYQQQghxZjmjrt00GAw89NBDLRq4ipZkG7VOtk/bZBu1TbZR22Qbta63b58eN7CcEEIIIfq2M6rmQwghhBCxJ+FDCCGEEN0q5j2cHi8YDFJeXt4l/XwIIYQQomsc28+HWt163UaPCx/l5eXSu6kQQgjRS5WWlrbZX1ePCx9msxmgR/XaJkQ02Nw+9Bp1nx/ZUghxZmrudbV5P96aDoePNWvW8Pjjj1NUVERFRQXvvPNOpBt1CFW7PPTQQzz33HM0NjYyffp0nnnmGQYPHtyu9TefaulpvbYJ0Rl7q+x8+5mN5CbF8d786Ri0EkCEEH1Te5pMdLjBqdPpZOzYsTz99NMnffyxxx7jL3/5C88++yzr168nPj6eiy66CLe7a0eBFaKncvsCzH91E3a3n92Vdv71RUmsiySEEDHV4ZqPSy65hEsuueSkjymKwuLFi/nVr37FVVddBcCLL75IZmYm7777Lt/5znc6V1oheqFH399JcZUdvVaN1x/kqZV7+faEXDITjbEumhBCxERUL7U9ePAglZWVzJ49O7LMYrEwdepU1q5de9LXeDwebDZbi0mIvuKDrRW8uv4wKhX8Y+4kxuUn4fQG+MNHu2NdNCGEiJmoho/KykqAEwaQy8zMjDx2vEWLFmGxWCKTXOki+orSehcPvL0VgDvPG8iMIek8cuVIAN7edISiQw2xLJ4QQsRMzDsZW7hwIVarNTKVlpbGukhCdJovEOSe10LtPCb2T+a+C4cAMDY/iesnhS5Be3jJDoJBGd1ACHHmiWr4yMrKAqCqqqrF8qqqqshjxzMYDJErW+QKF9FX/PGTYjaXNpJo1PJ/3xmHTnP0q/bzi4ZhNmjZdsTKm0UStoUQZ56oho+CggKysrJYsWJFZJnNZmP9+vVMmzYtmm8lRI+1ek8Nf1t9AIDHrh1DXrKpxePpZgM/mR269PyxZcVYm3zdXkYhhIilDocPh8PB5s2b2bx5MxBqZLp582YOHz6MSqXi3nvv5be//S1Llixh27ZtzJ07l5ycnBZ9gQjRV1Xb3Cx4fTMA3z+rPxePyj7p8+ZOG8DA9HjqnF7+smJvN5ZQCCFir8PhY+PGjYwfP57x48cDsGDBAsaPH8+DDz4IwP33388999zDj370IyZPnozD4WDZsmUYjXJZoejbgkGF+97YTJ3Ty7AsM/9z2fBTPlevVfPgFaHGp//+qoS9VfbuKqYQQsScSlGUHtXizWazYbFYsFqt0v5D9CpPr9rH4x8XE6fTsPSecxiUkdDma374740s31XFuYPTePGWKTKYohCi1+rI/jvmV7sI0RdsLKnnz5/uAeDRq0a2K3gA/Pry4eg1aj7fW8unO6vafoEQQvQBEj6E6KRGl5ef/GczgaDC1eNyuHZi66M5Hqt/ajy3zSgA4Dcf7MTtC3RVMYUQoseQ8CFEJyiKwi/+u5UjjU0MSDXx22+N7vCpk7tmDiIz0UBpfRP/+PxAF5VUCCF6DgkfQnTCy+sO8fGOKnQaFU9+dwIJhg4Pl0S8QcsvLw01Tn161X4qrE3RLqYQQvQoEj6EOE07y2385oNdACy8ZDij8yynva4rx+YwqX8yTb4Aiz6UcV+EEH2bhA8hToPT42f+a9/g9Qe5YFgGN08f0Kn1qVQqHr5yJCoVLNlSztcH66NTUCGE6IEkfAhxGh5asoMDNU6yEo08ft3YqFwiOyrXwncm94usPyDjvggh+igJH0J00DubynirqAy1Cv7vO+NIiddHbd0/mzOERKOWXRU2/rPhcNTWK4QQPYmEDyE64GCtk1+9sx2An1wwhKmFqVFdf2qCgQXhEXD/+HExjS5vVNcvhBA9gYQPIdopEFS47/XNOL0BphakMP/8QV3yPjed1Z8hmQk0uHw8Ee64TAgh+hIJH0K00z+/OMDm0kbMRi2LvzMOjbprukLXatQ8FB735eX1h9ldaeuS9xFCiFiR8CFEOxyocfCnT0K1EL++bATZlrgufb/pg9K4ZFQWgaDCI0t20sOGYBJCiE6R8CFEG4LBUC+mHn+Qcwencd2k9nef3hm/vHQ4Bq2atQfq+Gh7Zbe8pxBCdAcJH0K04d9rS9hQ0kC8XsOib3e8+/TTlZ9i4vbzBgLwuw920eSVcV+EEH2DhA8hWnG4zsVjy4oBeODS4eQlm7r1/e88byA5FiNHGptY9NGubn1vIYToKhI+hDiF5tMtTb4AZxWm8L0p/bq9DHF6Db/91igAXlx7iP98LX1/CCF6PwkfQpzCaxsOs/ZAHXE6DX+4ZgzqLrq6pS3nD8uM9P3x6/e2U3RIul4XQvRuEj6EOIkjjU2RAd5+ftFQ+qfGx7Q882cN4pJRWfgCCre/9I2MfCuE6NUkfAhxHEVReOC/W3F4/Ezsn8y8swfEukio1Sr+eN1YhmWZqXV4uP2lItw+aYAqhOidJHwIcZw3i8r4fG8tBq2ax64d02WdiXVUvEHLc3MnkWTSsbXMysK3t0n/H0KIXknChxDHqLS6+c37OwFYcOEQBqYnxLhELeWnmPjrjRPQqFW8s+kI//j8YKyLJIQQHSbhQ4gwRVH4n3e2YXf7GZtn4dZzCmJdpJM6e1AaD14+AoBFH+1i9Z6aGJdICCE6RsKHEGHvbS5nxe5qdBoVj183Fq2m53495k7rzw2T8gkqcM+r33Cw1hnrIgkhRLv13F9XIbpRtd3Nw0t3APDj8wczJNMc4xK1TqVS8ejVI5nQLwmb289tL27E7vbFulgAVNvclEgYEkK0QsKHEMBD7+2g0eVjRHYid8wcGOvitItBq+HZmyaSlWhkX7WD+17fTDAYuwaoiqLw8rpDzHh8FbP+9Bm/eGsrtQ5PzMojhOi5JHyIM94HWyv4aHslWrWKx68bg64Hn245Xkaikb99fyJ6rZrlu6p5YvmemJSj2u7mlhc28Kt3t+P2BVEUeH1jKbMe/4x/fH4Arz8Yk3IJIXqm3vMrK0QXqHd6efC97QDcNXMgI3MsMS5Rx43NT+L33x4NwJMr9/HB1opuff+Pd1Ry8eLPWVVcg16r5leXDefNO6YxOteC3ePntx/s4uL/W8NnxdXdWi4hRM+lUnpYRwE2mw2LxYLVaiUxMTHWxRF93I9f28SSLeUMyUxg6T3nYNBqYl2k0/a7D3by3OcHidNp+O+dZzMip2u/Pw6Pn0eX7uCNjWUADM9OZPEN4xiaFWovEwgqvFVUymPLiqlzegGYPTyDX102ggFpse0xVggRfR3Zf0vNhzhjfbKjkiVbylGr4PFrx/bq4AHwi4uHce7gNJp8AW57cSP14R1+Vyg6VM+l//c5b2wsQ6WC288r5N27z44EDwCNWsUNk/ux8mczufWcArRqFct3VTPniTX8/qPdODz+LiufEKJnk5oP0av4A0EO17vYW+1gX7WDOoeXlHgdaQmG0GQ2kBqvJ91swKg7dZiwunzMfmI1NXYPd5w3kAcuGdaNn6LrWF0+rnr6C0rqXJxVmMJLt06NahsWXyDI/y3fy18/20dQgdykOP50/VjOKkxt87X7qu08+v4u1oT7JckwG/jFxcP41vjcmA3aJ4SIno7svyV8iB7J4w9wsNbJvmoHe6sc7KtxsK/KwcFaJ95A+xovJhi0pCXoSU0wkJagbxFQPt9Twyc7qyhMj+fDH5/balDpbfZW2bn66S9xegPMm9afR64aFZX17qu2c9/rW9h2xArAt8fn8vBVI0k06tq9DkVRWLGrmt98sJNDdS4AxuUn8ciVIxmbnxSVcvZlvkCQQ3VOdBo1Rp0mPKnRa9SoVBLgRGxJ+BCnxenxU2P3kJsc121XfCiKwu5KO7sqbKGgUe1gf7WDQ/UuAqe4bNSoUzMoI4FB6QlkJBppcHqpdXiodYTmdQ5vuwKKSgVv3TGNif1Tov2xYu7TnVXc9uJGAH42ZwhzRmbRP9V0WqeWFEXhxbWH+N8Pd+HxB0ky6fjd1aO5bEz2aZfP4w/wry9KeGrlXpze0AB5107M4/6Lh5JhNp72evuq8sYm/vP1Yf6zoZRq+4mXL6tVYNRpiDsmkLS8H1o2NNPMVeNy6ZdqisGnENHi9gXYW+XA6fUzoV8yem3PaEEh4aOHcvsC1Du9J59cXuod4bnTS4PTi83tIyVeT7YljpwkIzmWOLKT4sixGCPztARDu6usm7wBjjS6KG1ooqzeRVlDE6UNoXlZQ1OkjUCyScclo7O5cmwOkwekdMnAanuq7CzZXM7SreWRI+DjmY1aBmUkMDgjITw3MygjgdykuFY/s6Io2Nz+UCCxe6hrDid2DzUOL3UODw0uL5eMyuaWHtqFejQ8uWIvf/r06KW3ahX0SzExMD2BgeHwNjAjnoHpCSSZ9CddR5XNzc/f2ho5VXLu4DT+eN1YMhOjExCqbG7+sGw3b39zBAjVVk0bmEphWjwFafEMSIunMC2edLPhjDuyDwYV1uyt4ZX1h1mxq4rmLB6n06BWQZMvwOl26zKxfzJXj8vhsjE5pMSf/G8v2scXCFJj9+D1B0k3G4g3aKO2bn8gyKF6F8WVdnZX2tlTaae4ys6hOmfkb2+J03HRyEwuH5PD2QNTY9ozs4SPk2jyBlh/sA69Ro1Wo0arUYVvq9Cq1eg0KnTh+zp1eK5Ro9Oo0ahVKIqCxx/E5vZha/Jjd/uwu/3hyReZ28LLbG5fZHmjy0eDy4vLG/0h0HUaFVkWYyigNIeSpDjUKkLhov5ouGhPh086jQpf4Oi/RGaigctG53DluBzG5lk6tQMorXexZEs5S7eUs7vSHllu1KkZk5fE4EjQMDM4M4GMM3CHE02KovD/7d17cFTl+Qfw716yt+xuLiS7YbMBuQa5BQGJYZBQRS5VUDudOpYCWoZOf1M6dHQUGQF/4FAcHKwdkUI7JbRVwKGjEvODWBpQiQZQciPcEiAh5EYuJJtkN3t/fn/s5iSH3HV3s8LzmTk52fe8e3bPk5NznvO+79l9/9Q1nLhcjxv17WjrZ4DniEiFPynxJSPj4rWwdLjwv5/5PnxNKZdi49JJWJX2QFDGZ+RXNmNr5kUUVVl6XR6pkOEBf0Jy99RX4hRoNqevZbC+zeGbt9rh9hKmm6Mx3RwVsK67pnYHjpyvwsGzlai805WYPzI2Fr96ZDQWTU6AQi4FEcHlIdjdHtidHthdXnS4PLC7PMLcN3lhd3nQ7nDjy9IGfH2tUThxyaUSLEiOxzMPJWLhg8YfXfejx0tosTnR6L+oaLQ60djmgNPjRaRSDq1ShkiFHFqlHJH+yfe7r7y/fdnu8vj/3nbcbvX9vev9f//6tq7Hdw/s1ihkMOiUiNcpYdCpEO//3fe4qzw2UiFc2BER6lrtuFrX5ptu++Zl9e19fkZObKQCUgnQ2O4UlS2ZmoCnpo1E6tgRIf9G7rBIPt5//328/fbbqKurQ0pKCt577z3MmTNnwOcFK/m40dCOx3Z9+b2eK5EAUomkz26AoYiQSRAbqUCMRoERWv88UoGYyK55rH/SqSLQ2OZAraUDNS12YV5j6UBtix31bfYhX/lolXKYY9RIitX45jG+uTlGA3OsGpoIGfJuNOGzohocL6lDm73rhJUUq8ay6SYsSzFhUoJuUIlBfZsd/1dci8yiGhRUtojikD4xHstSTFj4oDGgVwusJyJCQ5sD1xracb3Biuv17bje4OviqrHY+33uFJPvFtoJQf7Iea+XcKa8CWX+sT03Gq2oaLSiqtnW734eo4nAmLhImKLV0Cj8XQ3+uTpCBrVCJnRB9HiskEEpl8LS4RInFm124ffOqb+7c+RSCaaY9HhoVAxmjY7BzNExMEWpBp08ExG+u9mMD8/cxLELdUK3oU4lx89nmbEidRTGGwIT/9utdnxWVINPCqpxsaZVKNcq5Vg6NQHPPJSIR37giYuI0GJzoaq5Q4ibRAJI4PtqgM6wSPzlgERYDn8dL/kTizYnGq0ONLY50WR1CF2rje1O3LE6vnfrD+BLFLonJOoIGZptLtS32tFqH/zdWHKpBAq5dEgXmDKpBCP8x/paix2Wjt6/HkEdIcNEoxbJCTpMNOowKUGP5AQd4rQKeAk4V34HWcW+43X3RChOq8RPpyXgqekmzB4dE5JB3cOefHz00UdYtWoV9u7di9TUVLz77rs4cuQIrl69CoPB0O9zg5V8VDbZ8D8fnofL44XbQ3B5vXC5CW6vFy4Picr7i4hE4vsn1asioFN1zX1ThGiuV/vmUeoIYSfTKuUBu5p3ebyob3OgpqUDNS0dqLXYUdvSgeoWO4hIlGSYYzRIitFArx786zvcHnxV2ojPimpw4tJtdLi6/rEmGLRYluJLRMbc9ZkNFpsL2Rd9CUfe9Sbh4CCRAGljR2B5iglLpiaE7IqV9c/qcKO80SokI9ca2nG93oomqxPPPWzG+scnDmufssPtwa07HSj3JyOdSUl5oxV1rf0nToGmipDCoFMJV7BeIuRXtqChl3EYCXoVZo6OxsxRvmRkiknfY8xNm92FTwuq8cGZSly93dUaON0chV+ljsayFBPUiuC1RpTdbsOnhdX4tKAG1S0dQrlRr8TTMxLxzIxEPDiy54UGEcHS4fK3qna1rna1tNqEsTyhEKOJwAit7063OJ0SSpkUVqcbVoevxcfqn9odblidnkFfSCrkUhj1SuFvbtApYdCrxHOdEjEaBaRSiTBurqHdgfpWBxq6JbH13ZLbJqujx3lGJpVgbFwkJiboMMmo880TdEiK0QwqcXB7vMi70YSsolpkX6wTJTNGvRI/nTYST003Yeao6KC1KA978pGamoqHH34Yu3fvBgB4vV4kJSXh97//PV577bV+nzvcYz6ICB4vwe3tlpB4vPASBtVUd6+yOd04eaUemYU1+OJqg2hA57TEKCxLGQmjXoXPimrxZWm9qOtmRlI0lqeY8NT0kTAEaKwAY4Bvv6xotKGiyYo6i13ogujwdz10OL1CN0SH0yPqluh87HB5oVPJuzWNq+5qIu9qNu/t4oGIUNXcgfzKZuTfbEZ+ZQsu1bb2OMEp5FJMS4zCLH8icubGHRwtrBaullURUjydkogVj4zCdHN0qEIIwNfq9N3NZnxSUI1jF2pFJ65kow5PTDai3eEWEovq5o5+u/E6xeuU0Kt8rZrk/9EZFSICASACCCScjLufkWIiIzAi0n+Xmv+OtRHd5vFaJWIiFUMaIN/Zhd7ePSFxeGB1uGFzehCtifAnFaohXawNhdvjxR2rE/VtvpYcg06FcYbIgH3WkMvjRe61RmQV1eI/l8Qt2InRaqFFZPoP7Eq/27AmH06nExqNBv/+97/xzDPPCOWrV69GS0sLjh49KqrvcDjgcHRdNbS2tiIpKemeHHB6r2i1u/Cfi7eRWVSDr6819noVkWzUYfkME5ZNN/HIenbfsTndKK6y4PzNZhRUNuP8zWY023pvVh9v0OJXqaPw7EwzotSDv205WBxuD7642oBPC6qRc7m+3zvH4rRKf8uqv+u2W2trYrT6RzeG5F7kcHtwurQRWcW+FuzOFim9So7vNj0R0FbNoSQfAe9ob2xshMfjgdFoFJUbjUZcuXKlR/0dO3Zg69atgX4bLIj0qgj8fJYZP59lRlO7A8dL6pBVXINmqwsLJxuwPCVR9EmXjN1vNAo5Hhk7QvjwNSJCRZMN5282I7+yGSXVFoweEYkVqaOQOiY2rAZWK+UyLJ6SgMVTEmCxuXC8pBbfVjQjTqcQjRFLjFYHtUuIBYZSLsPCyUYsnGyE3eVLLLOKaxCnVQ5rd2rAWz5qamqQmJiIb775BmlpaUL5q6++ii+//BJnz54V1b+75cNisWDUqFG4desWt3wwxhhjPxKdPRctLS2Iiur/SzoD3vIRFxcHmUyG27dvi8pv376NhISEHvWVSiWUSqXwuLXVN/o6KSkp0G+NMcYYY0HW1tYW+uRDoVBg1qxZyMnJEcZ8eL1e5OTkYN26dQM+32Qy4datW9DpBncr572sM4vkVqDecXwGxjEaGMeofxyfgXGMfIgIbW1tMJlMA9YNyocrvPTSS1i9ejVmz56NOXPm4N1334XVasWLL7444HOlUinMZnMw3taPll6vv6936IFwfAbGMRoYx6h/HJ+BcYwwYItHp6AkH8899xwaGhqwZcsW1NXVYcaMGcjOzu4xCJUxxhhj95+gfazkunXrBtXNwhhjjLH7S3h8FR7rlVKpxBtvvCEakMu6cHwGxjEaGMeofxyfgXGMhi7svliOMcYYY/c2bvlgjDHGWEhx8sEYY4yxkOLkgzHGGGMhxckHY4wxxkKKk48A8Xg82Lx5M8aMGQO1Wo1x48bhzTffRPfxvO3t7Vi3bh3MZjPUajUmT56MvXv3itazYMECSCQS0fTb3/5WVCcnJwdz586FTqdDQkICNmzYALe76yuTKyoqeqxDIpHgzJkzwQ3CAAIVIwDIy8vDY489hsjISOj1esyfPx8dHR3C8jt37mDFihXQ6/WIjo7GmjVr0N7eLlpHcXExHn30UahUKiQlJWHnzp3B2/hBCqcY8X4EbN++HXPnzoVGo0F0dHSv76eyshJPPvkkNBoNDAYDXnnlFdH/Y6iFW3x624cOHz4c8O0eilDFqKKiAmvWrBG9zhtvvAGn0ylaRzgei4KOWEBs376dRowYQVlZWVReXk5HjhwhrVZLf/7zn4U6a9eupXHjxtGpU6eovLyc9u3bRzKZjI4ePSrUSU9Pp7Vr11Jtba0wWSwWYXlhYSEpFAraunUrlZWV0RdffEGTJk2il19+WahTXl5OAOi///2vaD1OpzM0wehDoGL0zTffkF6vpx07dlBJSQlduXKFPvroI7Lb7UKdJUuWUEpKCp05c4ZOnz5N48ePp+eff15YbrFYyGg00ooVK6ikpIQOHTpEarWa9u3bF5pg9CGcYsT7EdGWLVvonXfeoZdeeomioqJ6vBe3201Tp06lhQsXUkFBAR07dozi4uJo48aNQY1Bf8IpPkREACgjI0O0D3V0dARt+wcjVDE6fvw4vfDCC/T555/T9evX6ejRo2QwGETH63A9FgUbJx8B8uSTT9Kvf/1rUdnPfvYzWrFihfB4ypQptG3bNlGdmTNn0uuvvy48Tk9Pp/Xr1/f5Ohs3bqTZs2eLyjIzM0mlUlFraysRdZ00CgoKvufWBEegYpSamkqbNm3q83UuXbpEAOjbb78Vyo4fP04SiYSqq6uJiGjPnj0UExNDDodDqLNhwwZKTk7+fhsXIOEUo/t9P+ouIyOj15PrsWPHSCqVUl1dnVD2l7/8hfR6vWjfCqVwig+RL/n45JNPBvfmQ2Q4YtRp586dNGbMGOFxuB6Lgo27XQJk7ty5yMnJQWlpKQCgqKgIubm5WLp0qahOZmYmqqurQUQ4deoUSktLsWjRItG6PvzwQ8TFxWHq1KnYuHEjbDabsMzhcEClUonqq9Vq2O12nD9/XlS+fPlyGAwGzJs3D5mZmYHe5CELRIzq6+tx9uxZGAwGzJ07F0ajEenp6cjNzRXWkZeXh+joaMyePVsoW7hwIaRSKc6ePSvUmT9/PhQKhVBn8eLFuHr1Kpqbm4Mah/6EU4w63a/70WDk5eVh2rRpoq+OWLx4MVpbW3Hx4sUAbO3QhVN8Ov3ud79DXFwc5syZg/3794u6N4bDcMbIYrEgNjZWeByux6KgG8bE557i8Xhow4YNJJFISC6Xk0QioT/+8Y+iOna7nVatWkUASC6Xk0KhoH/84x+iOvv27aPs7GwqLi6mDz74gBITE+nZZ58Vln/++ecklUrp4MGD5Ha7qaqqih599FECQAcPHiQiooaGBtq1axedOXOGzp07J7yv7s2FwyEQMcrLyyMAFBsbS/v376f8/Hz6wx/+QAqFgkpLS4nI16Q6ceLEHq8fHx9Pe/bsISKiJ554gn7zm9+Ill+8eJEA0KVLlwK96YMWTjG63/ej7vq6sl+7di0tWrRIVGa1WgkAHTt2LDAbPEThFB8iom3btlFubi7l5+fTW2+9RUqlUtS9MRyGI0ZERGVlZaTX6+mvf/2rUBaux6Jg4+QjQA4dOkRms5kOHTpExcXF9M9//pNiY2PpwIEDQp23336bJk6cSJmZmVRUVETvvfceabVaOnHiRJ/rzcnJIQB07do1oWzXrl2k1+tJJpORRqOhHTt2EAA6fPhwn+tZuXIlzZs3LzAb+z0FIkZff/01AejRpz5t2jR67bXXiOjHnXyEU4x6cz/tR939mJKPcIpPbzZv3kxms/n7b2AADEeMqqqqaNy4cbRmzRpRebgei4KNk48AMZvNtHv3blHZm2++KfTb2Ww2ioiIoKysLFGdNWvW0OLFi/tcb3t7OwGg7OxsUbnX66Xq6mqy2WxC//25c+f6XM/u3bspISFhqJsVUIGI0Y0bNwgA/etf/xLV+cUvfkG//OUviYjo73//O0VHR4uWu1wukslk9PHHHxOR7yT69NNPi+qcPHmSANCdO3d+2Ib+AOEUo97cT/tRd32dXDdv3kwpKSmiss515+fnD3XTAiKc4tObrKwsAiAauBpqoY5RdXU1TZgwgVauXEkej0e0LFyPRcHGYz4CxGazQSoVh1Mmk8Hr9QIAXC4XXC5Xv3V6U1hYCAAYOXKkqFwikcBkMkGtVuPQoUNISkrCzJkz+13P3esItUDE6IEHHoDJZMLVq1dFdUpLSzF69GgAQFpaGlpaWkRjYE6ePAmv14vU1FShzldffQWXyyXUOXHiBJKTkxETExOgLR66cIpRb+6n/Wgw0tLScOHCBdTX1wtlJ06cgF6vx+TJk4e0XYESTvHpTWFhIWJiYob1S9hCGaPq6mosWLAAs2bNQkZGRo91huuxKOiGO/u5V6xevZoSExOFW7c+/vhjiouLo1dffVWok56eTlOmTKFTp07RjRs3KCMjg1QqldDMfe3aNdq2bRt99913VF5eTkePHqWxY8fS/PnzRa+1c+dOKi4uppKSEtq2bRtFRESIRpMfOHCADh48SJcvX6bLly/T9u3bSSqV0v79+0MSi74EIkZERH/6059Ir9fTkSNHqKysjDZt2kQqlUrUNbVkyRJ66KGH6OzZs5Sbm0sTJkwQ3Uba0tJCRqORVq5cSSUlJXT48GHSaDTDfntbOMWI9yOimzdvUkFBAW3dupW0Wi0VFBRQQUEBtbW1EVHXrbaLFi2iwsJCys7Opvj4+GG91Tac4pOZmUl/+9vf6MKFC1RWVkZ79uwhjUZDW7ZsCV1AehGqGFVVVdH48ePp8ccfp6qqKtHtxp3C9VgUbJx8BEhrayutX7+eRo0aRSqVisaOHUuvv/666Pap2tpaeuGFF8hkMpFKpaLk5GTatWsXeb1eIiKqrKyk+fPnU2xsLCmVSho/fjy98soros/5ICL6yU9+QlFRUaRSqSg1NbVH3/KBAwfowQcfJI1GQ3q9nubMmUNHjhwJfhAGEIgYddqxYweZzWbSaDSUlpZGp0+fFi1vamqi559/nrRaLen1enrxxReFA2KnoqIimjdvHimVSkpMTKS33noreBs/SOEUI96PfCcpAD2mU6dOCXUqKipo6dKlpFarKS4ujl5++WVyuVxBjUF/wik+x48fpxkzZpBWq6XIyEhKSUmhvXv39uh6CLVQxSgjI6PX+Nx93R+Ox6JgkxAN8z1PjDHGGLuv8JgPxhhjjIUUJx+MMcYYCylOPhhjjDEWUpx8MMYYYyykOPlgjDHGWEhx8sEYY4yxkOLkgzHGGGMhxckHY4wxxkKKkw/GGGOMhRQnH4wxxhgLKU4+GGOMMRZSnHwwxhhjLKT+H24aMdTGO0VNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(6):\n",
    "    _,cid,pid,off = r[i]\n",
    "    c3 = Cell(cid,nof_prb=100).cs_tx(pid)\n",
    "    off16 = off*16\n",
    "    wv = aLTE.waveform[:off16+307200+1024]\n",
    "    xc = np.abs(signal.correlate(aLTE.waveform,c3.reshape(307200,),'valid'))\n",
    "    pl.subplot(6,1,i+1)\n",
    "    pl.plot(np.arange(off16-16,off16+16),xc[off16-16:off16+16])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11.3 ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "160761bc1a904adbbd606c3a9b02244365b1a540f860a2c66cbc5cbd24c54900"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
